{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 调试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from lxml import etree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "headers={\n",
    "    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n",
    "    'Accept-Encoding': 'gzip, deflate, br',\n",
    "    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',\n",
    "    'Cache-Control': 'max-age=0',\n",
    "    'Connection': 'keep-alive',\n",
    "    'DNT': '1',\n",
    "    'Host': 'www.aaai.org',\n",
    "    'Referer': 'https://www.aaai.org/Library/conferences-library.php',\n",
    "    'Sec-Fetch-Mode': 'navigate',\n",
    "    'Sec-Fetch-Site': 'same-origin',\n",
    "    'Sec-Fetch-User': '?1',\n",
    "    'Upgrade-Insecure-Requests': '1',\n",
    "    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.117 Safari/537.36',\n",
    "    }\n",
    "cookies = {}\n",
    "proxies = { \"http\": \"http://127.0.0.1:1081\", \"https\": \"http://127.0.0.1:1081\", }\n",
    "proxies = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests, json\n",
    "from lxml import etree \n",
    "def get_AAAI_urls(years, write_to_json = False):\n",
    "    org = 'AAAI'\n",
    "    res_dict = {}\n",
    "    for year in years:\n",
    "        start_url = f'https://www.aaai.org/Library/AAAI/aaai{year[-2:]}contents.php'\n",
    "        names_list = []\n",
    "        urls_list_1 = []\n",
    "        pdf_download_url = []\n",
    "        with requests.Session() as s:\n",
    "            response1 = s.get(start_url, headers = headers, cookies = cookies, proxies=proxies)\n",
    "            x_path = '//a[contains(@href, \"/paper/view/\")]' if year != '2019' else '//a[text()=\"PDF\"]/@href/../../a[1]'\n",
    "            for item in etree.HTML(response1.text).xpath(x_path):\n",
    "                names_list.append(item.xpath('text()')[0].strip())\n",
    "                urls_list_1.append(item.xpath('@href')[0].strip())\n",
    "\n",
    "            print(len(urls_list_1))\n",
    "            for url in urls_list_1:\n",
    "                response2 = s.get(url, headers = headers, cookies = cookies, proxies=proxies, allow_redirects=True)\n",
    "                href = etree.HTML(response2.text).xpath('//a[contains(text(), \"PDF\")]/@href')[0]\n",
    "                pdf_download_url.append(href)\n",
    "            \n",
    "            print(len(pdf_download_url))\n",
    "            org_year = f'{org}_{year}'\n",
    "            res_dict[org_year] = {}\n",
    "            for name,url in zip(names_list,pdf_download_url):\n",
    "                res_dict[org_year][name] = url\n",
    "\n",
    "    if not write_to_json: return res_dict\n",
    "    temp = '_'.join(years)\n",
    "    file_name = f'{org}_{temp}.json'\n",
    "    with open(file_name, 'w', encoding='utf8') as f:\n",
    "        json.dump(res_dict, f)\n",
    "    print(f'DONE! write to: {file_name}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "474\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'xpath'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-7daa0f1be193>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0myears\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2014\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2020\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mget_AAAI_urls\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myears\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwrite_to_json\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-12-a1657d6399e0>\u001b[0m in \u001b[0;36mget_AAAI_urls\u001b[1;34m(years, write_to_json)\u001b[0m\n\u001b[0;32m     19\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0murl\u001b[0m \u001b[1;32min\u001b[0m \u001b[0murls_list_1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m                 \u001b[0mresponse2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcookies\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcookies\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_redirects\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m                 \u001b[0mhref\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0metree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mHTML\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxpath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'//a[contains(text(), \"PDF\")]/@href'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m                 \u001b[0mpdf_download_url\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhref\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'xpath'"
     ]
    }
   ],
   "source": [
    "years = [str(i) for i in range(2014,2020)]\n",
    "get_AAAI_urls(years, write_to_json = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "headers={\n",
    "'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n",
    "'Accept-Encoding': 'gzip, deflate, br',\n",
    "'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',\n",
    "'Connection': 'keep-alive',\n",
    "'DNT': '1',\n",
    "'Host': 'www.aaai.org',\n",
    "'Referer': 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8273',\n",
    "'Sec-Fetch-Mode': 'nested-navigate',\n",
    "'Sec-Fetch-Site': 'same-origin',\n",
    "'Upgrade-Insecure-Requests': '1',\n",
    "'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.117 Safari/537.36',\n",
    "    }\n",
    "cookies = {'Cookie': 'OCSSID=of5ha6i3q70ldmfi7ukleo20f4',}\n",
    "proxies = { \"http\": \"http://127.0.0.1:1081\", \"https\": \"http://127.0.0.1:1081\", }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from multiprocessing.dummy import Pool\n",
    "import requests, json\n",
    "from lxml import etree\n",
    "from utils import get_headers\n",
    "\n",
    "# pip install requests[socks] -U\n",
    "proxies = {\n",
    "    'http': 'socks5://127.0.0.1:1080',\n",
    "    'https': 'socks5://127.0.0.1:1080'\n",
    "}\n",
    "\n",
    "\n",
    "def get_AAAI_urls(years, write_to_json = False):\n",
    "    org = 'AAAI'\n",
    "    res_dict = {}\n",
    "    for year in years:\n",
    "        start_url = f'https://www.aaai.org/Library/AAAI/aaai{year[-2:]}contents.php'\n",
    "        names_list = []\n",
    "        urls_list_1 = []\n",
    "        pdf_download_url = []\n",
    "        with requests.Session() as s:\n",
    "            response1 = s.get(start_url, headers = headers, cookies = cookies, proxies=proxies)\n",
    "            x_path = '//a[contains(@href, \"/paper/view/\")]' if year != '2019' else '//a[text()=\"PDF\"]/@href/../../a[1]'\n",
    "            for item in etree.HTML(response1.text).xpath(x_path):\n",
    "                names_list.append(item.xpath('text()')[0].strip())\n",
    "                urls_list_1.append(item.xpath('@href')[0].strip())\n",
    "\n",
    "            print(len(urls_list_1))\n",
    "            \n",
    "            # multi-threadings\n",
    "            def _get_pdf_url(zip_args):\n",
    "                url = zip_args[0]\n",
    "                headers = zip_args[1]\n",
    "                cookies = zip_args[2]\n",
    "                proxies = zip_args[3]\n",
    "                import requests\n",
    "                with requests.Session() as s:\n",
    "                    response2 = s.get(url, headers = headers, cookies = cookies, proxies=proxies, allow_redirects=True, timeout = 30)\n",
    "                try:\n",
    "                    href = etree.HTML(response2.text).xpath('//a[contains(text(), \"PDF\")]/@href')[0]\n",
    "                    return href\n",
    "                except:\n",
    "                    return f'ERROR :NO XPATH PDF: {url}'\n",
    "            \n",
    "            # zip with urls_list_1\n",
    "            _url_list = [i.replace('view', 'viewPaper') for i in urls_list_1]\n",
    "            _headers = [get_headers(org = org, year = year, which = 2, code = i.split('/')[-1]) for i in urls_list_1]\n",
    "            _cookies = [cookies for _ in urls_list_1]\n",
    "            _proxies = [proxies for _ in urls_list_1]\n",
    "            arg1 = zip(_url_list, _headers, _cookies, _proxies)\n",
    "            with Pool(16) as pool:\n",
    "                iters = pool.imap(_get_pdf_url, arg1)\n",
    "                for it in iters:\n",
    "                    if 'ERROR ' in it: print(it)\n",
    "                    else: pdf_download_url.append(it)\n",
    "\n",
    "            print(len(pdf_download_url))\n",
    "            org_year = f'{org}_{year}'\n",
    "            res_dict[org_year] = {}\n",
    "            for name,url in zip(names_list,pdf_download_url):\n",
    "                res_dict[org_year][name] = url\n",
    "\n",
    "    if not write_to_json: return res_dict\n",
    "    temp = '_'.join(years)\n",
    "    file_name = f'{org}_{temp}.json'\n",
    "    with open(file_name, 'w', encoding='utf8') as f:\n",
    "        json.dump(res_dict, f)\n",
    "    print(f'DONE! write to: {file_name}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'headers' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-7daa0f1be193>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0myears\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2014\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2020\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mget_AAAI_urls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myears\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrite_to_json\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-1-1f89ca43a002>\u001b[0m in \u001b[0;36mget_AAAI_urls\u001b[0;34m(years, write_to_json)\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mpdf_download_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0mresponse1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcookies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcookies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m             \u001b[0mx_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'//a[contains(@href, \"/paper/view/\")]'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0myear\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'2019'\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'//a[text()=\"PDF\"]/@href/../../a[1]'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0metree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxpath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'headers' is not defined"
     ]
    }
   ],
   "source": [
    "years = [str(i) for i in range(2014,2020)]\n",
    "get_AAAI_urls(years, write_to_json = True)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "pip install requests[socks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8570/8398\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from lxml import etree \n",
    "headers={\n",
    "    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',\n",
    "    'Accept-Encoding': 'gzip, deflate, br',\n",
    "    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',\n",
    "    'Connection': 'keep-alive',\n",
    "    'Cookie': 'OCSSID=9r5krtocddt02fmtr6evlou893',\n",
    "    'DNT': '1',\n",
    "    'Host': 'www.aaai.org',\n",
    "    'Referer': 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8273',\n",
    "    'Upgrade-Insecure-Requests': '1',\n",
    "    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36',\n",
    "    }\n",
    "cookies = {}\n",
    "proxies = {\n",
    "    'http': 'socks5://127.0.0.1:1080',\n",
    "    'https': 'socks5://127.0.0.1:1080'\n",
    "}\n",
    "# url = 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8186'\n",
    "url = 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewPaper/8570'\n",
    "with requests.Session() as s:\n",
    "    response2 = s.get(url, headers = headers, cookies=cookies, proxies=proxies, allow_redirects=True, timeout = 30)\n",
    "    href = etree.HTML(response2.content).xpath('//a[contains(text(), \"PDF\")]/@href')[0]\n",
    "    print(href)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8140/8423\n"
     ]
    }
   ],
   "source": [
    "href = etree.HTML(response2.content).xpath('//a[contains(text(), \"PDF\")]/@href')[0]\n",
    "print(href)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response2.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from multiprocessing.dummy import Pool\n",
    "import requests, json\n",
    "from lxml import etree\n",
    "from utils import get_headers_cookies\n",
    "\n",
    "# pip install requests[socks] -U\n",
    "proxies = {\n",
    "    'http': 'socks5://127.0.0.1:1080',\n",
    "    'https': 'socks5://127.0.0.1:1080'\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "474\n"
     ]
    }
   ],
   "source": [
    "org = 'AAAI'\n",
    "res_dict = {}\n",
    "year = '2014'\n",
    "\n",
    "start_url = f'https://www.aaai.org/Library/AAAI/aaai{year[-2:]}contents.php'\n",
    "names_list = []\n",
    "urls_list_1 = []\n",
    "pdf_download_url = []\n",
    "headers, cookies = get_headers_cookies(org = org, year=year, which = 1)\n",
    "with requests.Session() as s:\n",
    "    response1 = s.get(start_url, headers = headers, cookies = cookies, proxies=proxies)\n",
    "    x_path = '//a[contains(@href, \"/paper/view/\")]' if year != '2019' else '//a[text()=\"PDF\"]/@href/../../a[1]'\n",
    "    for item in etree.HTML(response1.text).xpath(x_path):\n",
    "        names_list.append(item.xpath('text()')[0].strip())\n",
    "        urls_list_1.append(item.xpath('@href')[0].strip())\n",
    "\n",
    "    print(len(urls_list_1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8273',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8138',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8227',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8213',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8271',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8485',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8530',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8570',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8467',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8398',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8402',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8305',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8194',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8164',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8583',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8133',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8350',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8361',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8505',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8455',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8451',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8599',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8353',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8649',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8155',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8260',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8209',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8150',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8462',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8331',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8140',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8267',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8315',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8368',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8617',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8308',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8160',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8645',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8314',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8520',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8370',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8461',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8393',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8396',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8489',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8436',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8218',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8147',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8342',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8262',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8316',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8466',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8518',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8630',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8161',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8660',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8659',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8481',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8577',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8468',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8647',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8220',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8431',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8208',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8541',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8268',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8310',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8629',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8333',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8421',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8423',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8539',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8516',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8152',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8572',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8534',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8352',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8193',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8330',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8285',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8154',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8373',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8616',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8407',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8605',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8409',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8162',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8589',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8399',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8585',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8496',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8511',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8272',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8561',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8459',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8419',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8549',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8186',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8306',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8355',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8426',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8387',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8507',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8336',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8346',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8412',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8243',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8478',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8416',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8474',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8488',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8470',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8307',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8532',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8182',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8618',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8597',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8294',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8591',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8596',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8216',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8497',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8637',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8389',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8244',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8425',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8291',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8542',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8538',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8579',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8515',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8320',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8391',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8238',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8328',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8460',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8453',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8231',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8487',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8625',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8552',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8575',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8191',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8590',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8195',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8293',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8371',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8175',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8201',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8651',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8245',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8524',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8269',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8531',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8239',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8270',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8582',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8232',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8510',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8631',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8344',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8594',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8592',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8338',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8256',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8608',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8369',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8606',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8362',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8222',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8144',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8240',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8145',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8189',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8536',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8571',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8493',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8286',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8527',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8519',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8403',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8359',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8236',\n",
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       " 'http://www.aaai.org/ocs/index.php/IAAI/IAAI14/paper/view/8301',\n",
       " 'http://www.aaai.org/ocs/index.php/IAAI/IAAI14/paper/view/8219',\n",
       " 'http://www.aaai.org/ocs/index.php/IAAI/IAAI14/paper/view/8612',\n",
       " 'http://www.aaai.org/ocs/index.php/IAAI/IAAI14/paper/view/8343',\n",
       " 'http://www.aaai.org/ocs/index.php/IAAI/IAAI14/paper/view/8363',\n",
       " 'http://www.aaai.org/ocs/index.php/IAAI/IAAI14/paper/view/8229',\n",
       " 'http://www.aaai.org/ocs/index.php/IAAI/IAAI14/paper/view/8576',\n",
       " 'http://www.aaai.org/ocs/index.php/IAAI/IAAI14/paper/view/8321',\n",
       " 'http://www.aaai.org/ocs/index.php/IAAI/IAAI14/paper/view/8432',\n",
       " 'http://www.aaai.org/ocs/index.php/IAAI/IAAI14/paper/view/8259',\n",
       " 'http://www.aaai.org/ocs/index.php/EAAI/EAAI14/paper/view/8445',\n",
       " 'http://www.aaai.org/ocs/index.php/EAAI/EAAI14/paper/view/8249',\n",
       " 'http://www.aaai.org/ocs/index.php/EAAI/EAAI14/paper/view/8584',\n",
       " 'http://www.aaai.org/ocs/index.php/EAAI/EAAI14/paper/view/8248',\n",
       " 'http://www.aaai.org/ocs/index.php/EAAI/EAAI14/paper/view/8613',\n",
       " 'http://www.aaai.org/ocs/index.php/EAAI/EAAI14/paper/view/8661',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8641',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8417',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8408',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8411',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8142',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8447',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8199',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8640',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8280',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8130',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8428',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8253',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8440',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8311',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8627',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8279',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8567',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8433',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8263',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8128',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8456',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8564',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8302',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8535',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8563',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8624',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8543',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8544',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8283',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8303',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8492',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8166',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8172',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8296',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8297',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8146',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8444',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8266',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8593',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8610',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8604',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8652',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8127',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8340',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8158',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8275',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8157',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8156',\n",
       " 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8265']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urls_list_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewPaper/8273', 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewPaper/8273', 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewPaper/8273', 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewPaper/8273')\n",
      "({'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8', 'Connection': 'keep-alive', 'Cookie': 'OCSSID=9r5krtocddt02fmtr6evlou893', 'DNT': '1', 'Host': 'www.aaai.org', 'Referer': 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8273', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'}, {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8', 'Connection': 'keep-alive', 'Cookie': 'OCSSID=9r5krtocddt02fmtr6evlou893', 'DNT': '1', 'Host': 'www.aaai.org', 'Referer': 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8273', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'}, {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8', 'Connection': 'keep-alive', 'Cookie': 'OCSSID=9r5krtocddt02fmtr6evlou893', 'DNT': '1', 'Host': 'www.aaai.org', 'Referer': 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8273', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'}, {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8', 'Connection': 'keep-alive', 'Cookie': 'OCSSID=9r5krtocddt02fmtr6evlou893', 'DNT': '1', 'Host': 'www.aaai.org', 'Referer': 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8273', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'})\n",
      "(None, None, None, None)\n",
      "({'http': 'socks5://127.0.0.1:1080', 'https': 'socks5://127.0.0.1:1080'}, {'http': 'socks5://127.0.0.1:1080', 'https': 'socks5://127.0.0.1:1080'}, {'http': 'socks5://127.0.0.1:1080', 'https': 'socks5://127.0.0.1:1080'}, {'http': 'socks5://127.0.0.1:1080', 'https': 'socks5://127.0.0.1:1080'})\n",
      "('http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewPaper/8138', 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewPaper/8138', 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewPaper/8138', 'http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewPaper/8138')\n",
      "({'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8', 'Connection': 'keep-alive', 'Cookie': 'OCSSID=9r5krtocddt02fmtr6evlou893', 'DNT': '1', 'Host': 'www.aaai.org', 'Referer': 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8138', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'}, {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8', 'Connection': 'keep-alive', 'Cookie': 'OCSSID=9r5krtocddt02fmtr6evlou893', 'DNT': '1', 'Host': 'www.aaai.org', 'Referer': 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8138', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'}, {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8', 'Connection': 'keep-alive', 'Cookie': 'OCSSID=9r5krtocddt02fmtr6evlou893', 'DNT': '1', 'Host': 'www.aaai.org', 'Referer': 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8138', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'}, {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8', 'Connection': 'keep-alive', 'Cookie': 'OCSSID=9r5krtocddt02fmtr6evlou893', 'DNT': '1', 'Host': 'www.aaai.org', 'Referer': 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8138', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'})\n",
      "(None, None, None, None)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "string indices must be integers",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-0818ce59c3d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpool\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0miters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_get_pdf_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzip_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m'ERROR '\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpdf_download_url\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/anaconda3/envs/tf2/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    746\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    747\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 748\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    749\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    750\u001b[0m     \u001b[0m__next__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m                    \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/anaconda3/envs/tf2/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mworker\u001b[0;34m(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0mjob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mwrap_exception\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_helper_reraises_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-0818ce59c3d5>\u001b[0m in \u001b[0;36m_get_pdf_url\u001b[0;34m(zip_args)\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mimport\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mresponse2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcookies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcookies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_redirects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mhref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0metree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxpath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'//a[contains(text(), \"PDF\")]/@href'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/anaconda3/envs/tf2/lib/python3.7/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, url, **kwargs)\u001b[0m\n\u001b[1;32m    544\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    545\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 546\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'GET'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/anaconda3/envs/tf2/lib/python3.7/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    517\u001b[0m             \u001b[0mhooks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhooks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    518\u001b[0m         )\n\u001b[0;32m--> 519\u001b[0;31m         \u001b[0mprep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    520\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    521\u001b[0m         \u001b[0mproxies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproxies\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/anaconda3/envs/tf2/lib/python3.7/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mprepare_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    438\u001b[0m         \u001b[0;31m# Bootstrap CookieJar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcookies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcookielib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCookieJar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m             \u001b[0mcookies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcookiejar_from_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcookies\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m         \u001b[0;31m# Merge with session cookies\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/anaconda3/envs/tf2/lib/python3.7/site-packages/requests/cookies.py\u001b[0m in \u001b[0;36mcookiejar_from_dict\u001b[0;34m(cookie_dict, cookiejar, overwrite)\u001b[0m\n\u001b[1;32m    522\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcookie_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0moverwrite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames_from_jar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 524\u001b[0;31m                 \u001b[0mcookiejar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_cookie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcreate_cookie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcookie_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcookiejar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
     ]
    }
   ],
   "source": [
    "# multi-threadings\n",
    "def _get_pdf_url(zip_args):\n",
    "    url = zip_args[0]\n",
    "    headers = zip_args[1]\n",
    "    cookies = zip_args[2]\n",
    "    proxies = zip_args[3]\n",
    "    print (zip_args)\n",
    "    import requests\n",
    "    with requests.Session() as s:\n",
    "        response2 = s.get(url, headers = headers, cookies = cookies, proxies=proxies, allow_redirects=True, timeout = 30)\n",
    "    try:\n",
    "        href = etree.HTML(response2.text).xpath('//a[contains(text(), \"PDF\")]/@href')[0]\n",
    "        return href\n",
    "    except:\n",
    "        return f'ERROR :NO XPATH PDF: {url}'\n",
    "\n",
    "# zip with urls_list_1\n",
    "_url_list = [] _headers = _cookies = _proxies = []\n",
    "for i in urls_list_1:\n",
    "    _url_list.append(i.replace('view', 'viewPaper'))\n",
    "    t1, t2 = get_headers_cookies(org = org, year = year, which = 2, code = i.split('/')[-1])\n",
    "    _headers.append(t1)\n",
    "    _cookies.append(t2)\n",
    "    _proxies.append(proxies)\n",
    "\n",
    "zip_args = zip(_url_list, _headers, _cookies, _proxies)\n",
    "with Pool(16) as pool:\n",
    "    iters = pool.imap(_get_pdf_url, zip_args)\n",
    "    for it in iters:\n",
    "        if 'ERROR ' in str(it): print(it)\n",
    "        else: pdf_download_url.append(it)\n",
    "\n",
    "print(len(pdf_download_url))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "_url_list = _headers = _cookies = _proxies = []\n",
    "_proxies.append('sss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sss']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_url_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0ss', '1ss', '2ss', '3ss', '4ss', '5ss', '6ss', '7ss', '8ss', '9ss']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def test(x):\n",
    "    return str(x)+'ss', str(x)+'aa'\n",
    "\n",
    "a1 = [test(i)[0] for i in range(10)]\n",
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-550b6cfe0056>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0metree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'response2.content'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxpath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'//a[contains(text(), \"PDF\")]/@href'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "href = etree.HTML('response2.content').xpath('//a[contains(text(), \"PDF\")]/@href')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import demjson\n",
    "\n",
    "with open('KDD_2017_2018_2019.json', 'r', encoding='utf8') as f:\n",
    "    json_obj = demjson.decode(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "demjson.encode_to_file('KDD_2017_2018_2019.json',json_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('KDD_2017_2018_2019.json', 'r', encoding='utf8') as f:\n",
    "    json_obj = json.loads(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "1122\n"
     ]
    }
   ],
   "source": [
    "url = 'https://www.aclweb.org/anthology/events/acl-2018/'\n",
    "with requests.Session() as s:\n",
    "    response = s.get(url, headers = headers, cookies = cookies)\n",
    "    print(response.status_code)\n",
    "    pdf_urls_2 = []\n",
    "    for item in etree.HTML(response.text).xpath('//a[contains(@href, \".pdf\")]/@href'):\n",
    "        pdf_urls_2.append(item)\n",
    "    print(len(pdf_urls_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.aclweb.org/anthology/P18-1000.pdf Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\n",
      "https://www.aclweb.org/anthology/P18-1001.pdf Probabilistic FastText for Multi-Sense Word Embeddings\n",
      "https://www.aclweb.org/anthology/P18-1002.pdf A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors\n",
      "https://www.aclweb.org/anthology/P18-1003.pdf Unsupervised Learning of Distributional Relation Vectors\n",
      "https://www.aclweb.org/anthology/P18-1004.pdf Explicit Retrofitting of Distributional Word Vectors\n",
      "https://www.aclweb.org/anthology/P18-1005.pdf Unsupervised Neural Machine Translation with Weight Sharing\n",
      "https://www.aclweb.org/anthology/P18-1006.pdf Triangular Architecture for Rare Language Translation\n",
      "https://www.aclweb.org/anthology/P18-1007.pdf Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates\n",
      "https://www.aclweb.org/anthology/P18-1008.pdf The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation\n",
      "https://www.aclweb.org/anthology/P18-1009.pdf Ultra-Fine Entity Typing\n"
     ]
    }
   ],
   "source": [
    "for node in etree.HTML(response.text).xpath('//a[contains(@href, \".pdf\")]/../..//strong')[:10]:\n",
    "    url = 'https://www.aclweb.org' + node.xpath('a/@href')[0][:-1] + '.pdf'\n",
    "    name = node.xpath('string(a)')\n",
    "    print (url, name)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'acl_2019': []}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = {}\n",
    "a['acl_2019'] = []\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "PermissionError",
     "evalue": "[WinError 5] 拒绝访问。: './aaa'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-c38ab3982abf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'./aaa'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'./aaa'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m: [WinError 5] 拒绝访问。: './aaa'"
     ]
    }
   ],
   "source": [
    "if not os.path.exists('./aaa'):\n",
    "    os.mkdir('./aaa')\n",
    "    \n",
    "os.remove('./aaa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdfs = \"https://www.aclweb.org/anthology/P19-1002.pdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 封装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_acl_all_pdf_urls(year):\n",
    "    headers={\n",
    "        'authority': 'www.aclweb.org',\n",
    "        'method': 'GET',\n",
    "        'path': f'/anthology/events/acl-{year}/',\n",
    "        'scheme': 'https',\n",
    "        'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',\n",
    "        'accept-encoding': 'gzip, deflate, br',\n",
    "        'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8',\n",
    "        'cache-control': 'max-age=0',\n",
    "        'dnt': '1',\n",
    "        'referer': 'https://www.aclweb.org/anthology/',\n",
    "        'upgrade-insecure-requests': '1',\n",
    "        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36',\n",
    "        }\n",
    "    url = f'https://www.aclweb.org/anthology/events/acl-{year}/'\n",
    "    with requests.Session() as s:\n",
    "        response = s.get(url, headers = headers)\n",
    "        print(f'{year} status code: {response.status_code}')\n",
    "    pdf_urls_dict = {}\n",
    "    for node in etree.HTML(response.text).xpath('//a[contains(@href, \".pdf\")]/../..//strong'):\n",
    "        url = 'https://www.aclweb.org' + node.xpath('a/@href')[0][:-1] + '.pdf'\n",
    "        name = node.xpath('string(a)')\n",
    "        pdf_urls_dict[name] = url\n",
    "    return pdf_urls_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_acl_papers(years = ['2019']):\n",
    "    res_dict = {}\n",
    "    for year in years:\n",
    "        res_dict[f'acl_{year}'] = get_acl_all_pdf_urls(year)\n",
    "    return res_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "200\n",
      "200\n",
      "200\n",
      "200\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "years = [str(i) for i in range(2014, 2020)]\n",
    "acl_pdfs_all = get_acl_papers(years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json,pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./acl_pdfs_all.json', 'w') as f:\n",
    "    json.dump(acl_pdfs_all, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "333\n",
      "363\n",
      "383\n",
      "357\n",
      "673\n",
      "1393\n"
     ]
    }
   ],
   "source": [
    "for k in acl_pdfs_all:\n",
    "    print(len(acl_pdfs_all[k]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 下载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3af566954ef447796b837c497694c77",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='acl_2014', max=333, style=ProgressStyle(description_width='in…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n",
      "From cffi callback <function _verify_callback at 0x000001C8C0A2C6A8>:\n",
      "Traceback (most recent call last):\n",
      "  File \"d:\\python37\\lib\\site-packages\\OpenSSL\\SSL.py\", line 306, in wrapper\n",
      "    @wraps(callback)\n",
      "KeyboardInterrupt\n",
      "d:\\python37\\lib\\site-packages\\urllib3\\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-171-e1a5b8c9ff13>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'{paper_year}/{pdf_name}.pdf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'{paper_year}/{pdf_name}.pdf'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m                     \u001b[1;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpdf_url\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstream\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"TRUE\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mverify\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miter_content\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m                         \u001b[0mfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m             \u001b[0mpbar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python37\\lib\\site-packages\\requests\\models.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m()\u001b[0m\n\u001b[0;32m    748\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'stream'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    749\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 750\u001b[1;33m                     \u001b[1;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunk_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    751\u001b[0m                         \u001b[1;32myield\u001b[0m \u001b[0mchunk\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    752\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mProtocolError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python37\\lib\\site-packages\\urllib3\\response.py\u001b[0m in \u001b[0;36mstream\u001b[1;34m(self, amt, decode_content)\u001b[0m\n\u001b[0;32m    529\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    530\u001b[0m             \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_fp_closed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mamt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdecode_content\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    532\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python37\\lib\\site-packages\\urllib3\\response.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[0;32m    477\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    478\u001b[0m                 \u001b[0mcache_content\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 479\u001b[1;33m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    480\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mamt\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# Platform-specific: Buggy versions of Python.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    481\u001b[0m                     \u001b[1;31m# Close the connection when no data is returned\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python37\\lib\\http\\client.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    445\u001b[0m             \u001b[1;31m# Amount is given, implement using readinto\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    446\u001b[0m             \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytearray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 447\u001b[1;33m             \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    448\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mmemoryview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    449\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python37\\lib\\http\\client.py\u001b[0m in \u001b[0;36mreadinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    489\u001b[0m         \u001b[1;31m# connection, and the user is reading more bytes than will be provided\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    490\u001b[0m         \u001b[1;31m# (for example, reading in 1k chunks)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 491\u001b[1;33m         \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    492\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    493\u001b[0m             \u001b[1;31m# Ideally, we would raise IncompleteRead if the content-length\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python37\\lib\\socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    587\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    588\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 589\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    590\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    591\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python37\\lib\\site-packages\\urllib3\\contrib\\pyopenssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    302\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    303\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 304\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    305\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mOpenSSL\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSSL\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSysCallError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Unexpected EOF'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python37\\lib\\site-packages\\OpenSSL\\SSL.py\u001b[0m in \u001b[0;36mrecv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m   1819\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_lib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSSL_peek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1820\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1821\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_lib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSSL_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1822\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raise_ssl_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1823\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import re\n",
    "from tqdm import tqdm_notebook\n",
    "for paper_year in acl_pdfs_all:\n",
    "    if not os.path.exists(paper_year): os.makedirs(paper_year)\n",
    "    with tqdm_notebook(total = len(acl_pdfs_all[paper_year].items()), desc = paper_year) as pbar:\n",
    "        for pdf_name, pdf_url in acl_pdfs_all[paper_year].items():\n",
    "            pdf_name = re.sub(r'[\\\\/:*?\"<>|]', \" \", pdf_name.strip())\n",
    "            if not os.path.exists(f'{paper_year}/{pdf_name}.pdf'): \n",
    "                with open(f'{paper_year}/{pdf_name}.pdf','wb') as file:\n",
    "                    for data in requests.get(pdf_url,stream=\"TRUE\",verify=False).iter_content():\n",
    "                        file.write(data)\n",
    "            pbar.update(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "48c00d12581d4f24aae0552ad86616ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3502), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from multiprocessing.dummy import Pool\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "def yield_tuples(acl_pdfs_all):\n",
    "    for paper_year in acl_pdfs_all:\n",
    "        for pdf_name, pdf_url in acl_pdfs_all[paper_year].items():\n",
    "            yield (paper_year, pdf_name, pdf_url)\n",
    "\n",
    "def download_multi(dw_tuple,pbar):\n",
    "    \"\"\"dw_tuple = ('acl_xxxx,name,url')\"\"\"\n",
    "    paper_year = dw_tuple[0]\n",
    "    pdf_name = re.sub(r'[\\\\/:*?\"<>|]', \" \", dw_tuple[1].strip())\n",
    "    pdf_url = dw_tuple[2]\n",
    "    headers={\n",
    "        'authority': 'www.aclweb.org',\n",
    "        'method': 'GET',\n",
    "        'path': f'/anthology/events/acl-{paper_year[-4:]}/',\n",
    "        'scheme': 'https',\n",
    "        'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',\n",
    "        'accept-encoding': 'gzip, deflate, br',\n",
    "        'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8',\n",
    "        'cache-control': 'max-age=0',\n",
    "        'dnt': '1',\n",
    "        'referer': 'https://www.aclweb.org/anthology/',\n",
    "        'upgrade-insecure-requests': '1',\n",
    "        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36',\n",
    "        }\n",
    "    if not os.path.exists(f'{paper_year}/{pdf_name}.pdf'): \n",
    "        with open(f'{paper_year}/{pdf_name}.pdf','wb') as file:\n",
    "            with requests.Session() as s:\n",
    "                for data in s.get(pdf_url, headers = headers, stream=\"TRUE\").iter_content():\n",
    "                    file.write(data)\n",
    "    pbar.update(1)\n",
    "                    \n",
    "pool = Pool(10)\n",
    "total = len([i for i in yield_tuples(acl_pdfs_all)])\n",
    "pbar = tqdm_notebook(total = total, desc)\n",
    "for tu in yield_tuples(acl_pdfs_all):\n",
    "    pool.apply_async(download_multi,args=(tu,pbar,))\n",
    "pool.close()\n",
    "pool.join()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S DDGFFSA  SSDSDDDDAA222DDDDDD'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "pdf_name = 'S DD\\GFF/SA : SSD?SD\"DDDAA<222>DDD||DD\\D'\n",
    "x = re.sub(r'[\\\\/:*?\"<>|]', \" \", pdf_name)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm, trange\n",
    "from tqdm import tqdm_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('s', '111'), ('a', 222)])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aaa = {'s':'111', 'a':222}\n",
    "aaa.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'k1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-160-6e31caf94c13>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'a11'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'name1'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'url1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'name2'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'url2'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'a22'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'name3'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'url3'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'name4'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'url4'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mk1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm_notebook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdesc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mk1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;31m#     print(k)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm_notebook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0murl\u001b[0m \u001b[1;32min\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkk1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'k1' is not defined"
     ]
    }
   ],
   "source": [
    "import time \n",
    "q = {'a11':{'name1':'url1', 'name2':'url2'},'a22':{'name3':'url3','name4':'url4'}}\n",
    "for k1,_ in tqdm_notebook([(k,v) for k,v in q.items()], desc = k1):\n",
    "#     print(k)\n",
    "    for i,j in tqdm_notebook([(name,url) for name,url in a[kk1].items()]):\n",
    "        time.sleep(2)\n",
    "        print(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd55565ea36c4bd2bdf1ba20a97e0345",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='a22', max=2, style=ProgressStyle(description_width='initial')…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "a22:   0%|<bar/>| 0/2 [00:00<?, ?it/s]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tqdm_notebook([(k,v) for k,v in q.items()], desc = k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|                                                              | 0/4 [00:00<?, ?it/s]\n",
      "\n",
      "\n",
      "\n",
      "Processing a:   0%|                                                | 0/4 [00:00<?, ?it/s]\n",
      "\n",
      "\n",
      "\n",
      "Processing b:   0%|                                                | 0/4 [00:00<?, ?it/s]\n",
      "\n",
      "\n",
      "\n",
      "Processing c:   0%|                                                | 0/4 [00:00<?, ?it/s]\n",
      "\n",
      "\n",
      "\n",
      "Processing d: 100%|███████████████████████████████████████| 4/4 [00:00<00:00, 398.56it/s]\n"
     ]
    }
   ],
   "source": [
    "pbar = tqdm([\"a\", \"b\", \"c\", \"d\"])\n",
    "for char in pbar:\n",
    "    pbar.set_description(\"Processing %s\" % char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tqdm.notebook.tqdm'; 'tqdm.notebook' is not a package",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-166-338a2bdf6f9a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnotebook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtqdm\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'pinfo'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'tqdm'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tqdm.notebook.tqdm'; 'tqdm.notebook' is not a package"
     ]
    }
   ],
   "source": [
    "import tqdm.notebook.tqdm as tqdm\n",
    "tqdm?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from PyPDF4 import PdfFileReader,PdfFileWriter\n",
    "\n",
    "url = 'https://www.aclweb.org/anthology/P14-1003.pdf'\n",
    "response = requests.get(url)\n",
    "with open('P14-1003.pdf', 'wb') as f:\n",
    "    f.write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Information about P14-1003.pdf\n",
      "    \n",
      "    Author: Sujian Li ; Liang Wang ; Ziqiang Cao ; Wenjie Li\n",
      "    Creator: LaTeX with hyperref package\n",
      "    Producer: pdfTeX-1.40.3\n",
      "    Subject: P14-1 2014\n",
      "    Title: Text-level Discourse Dependency Parsing\n",
      "    Number of pages: 11\n",
      "    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'PDF broken'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def extract_information(pdf_path):\n",
    "    try:\n",
    "        with open(pdf_path,'rb') as f:\n",
    "            pdf=PdfFileReader(f)\n",
    "            information=pdf.getDocumentInfo()\n",
    "            number_of_pages=pdf.getNumPages()\n",
    "    except:\n",
    "        return 'PDF broken'\n",
    "    txt=f\"\"\"\n",
    "    Information about {pdf_path}\n",
    "    \n",
    "    Author: {information.author}\n",
    "    Creator: {information.creator}\n",
    "    Producer: {information.producer}\n",
    "    Subject: {information.subject}\n",
    "    Title: {information.title}\n",
    "    Number of pages: {number_of_pages}\n",
    "    \"\"\"\n",
    "    print(txt)\n",
    "    return information\n",
    "\n",
    "extract_information('P14-1003.pdf')\n",
    "extract_information('./acl_2014/A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'bytes' object has no attribute 'seek'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-32824d08ad5c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mPdfFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32md:\\python37\\lib\\site-packages\\PyPDF4\\pdf.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, stream, strict, warndest, overwriteWarnings)\u001b[0m\n\u001b[0;32m   1146\u001b[0m             \u001b[0mstream\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1147\u001b[0m             \u001b[0mfileobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1148\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstream\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1149\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstream\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstream\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python37\\lib\\site-packages\\PyPDF4\\pdf.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, stream)\u001b[0m\n\u001b[0;32m   1752\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdebug\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\">>read\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1753\u001b[0m         \u001b[1;31m# start at the end:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1754\u001b[1;33m         \u001b[0mstream\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1755\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mstream\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtell\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1756\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPdfReadError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot read an empty file'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'bytes' object has no attribute 'seek'"
     ]
    }
   ],
   "source": [
    "PdfFileReader(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 status code: 200\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14553/13735 SnapNETS: Automatic Segmentation of Network Sequences with Node Labels\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14746/13736 Taming the Matthew Effect in Online Markets with Social Influence\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14192/13737 A Leukocyte Detection Technique in Blood Smear Images Using Plant Growth Simulation Algorithm\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14348/13738 Partitioned Sampling of Public Opinions Based on Their Social Dynamics\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14269/13739 Novel Geometric Approach for Global Alignment of PPI Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14561/13740 Towards Better Understanding the Clothing Fashion Styles: A Multimodal Deep Learning Approach\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14791/13741 Profit-Driven Team Grouping in Social Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14285/13742 Gated Neural Networks for Option Pricing: Rationality by Design\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14347/13743 Local Discriminant Hyperalignment for Multi-Subject fMRI Data Alignment\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14719/13744 Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14763/13745 StructInf: Mining Structural Influence from Social Streams\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14559/13746 Transitive Hashing Network for Heterogeneous Multimedia Retrieval\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14730/13747 Marrying Uncertainty and Time in Knowledge Graphs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14845/13748 TweetFit: Fusing Multiple Social Media and Sensor Data for Wellness Profile Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14902/13749 POI2Vec: Geographical Latent Representation for Predicting Future Visitors\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14878/13750 A Dependency-Based Neural Reordering Model for Statistical Machine Translation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14612/13751 Joint Identification of Network Communities and Semantics via Integrative Modeling of Network Topologies and Node Contents\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14863/13752 Random-Radius Ball Method for Estimating Closeness Centrality\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14772/13753 Read the Silence: Well-Timed Recommendation via Admixture Marked Point Processes\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14254/13754 Treatment Effect Estimation with Data-Driven Variable Decomposition\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14709/13755 A Declarative Approach to Data-Driven Fact Checking\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14508/13756 Semantic Proximity Search on Heterogeneous Graph by Proximity Embedding\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14712/13757 Multi-Task Deep Learning for User Intention Understanding in Speech Interaction Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14396/13758 Understanding the Semantic Structures of Tables with a Hybrid Deep Neural Network Architecture\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14199/13759 Radon – Rapid Discovery of Topological Relations\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14663/13760 Web-Based Semantic Fragment Discovery for On-Line Lingual-Visual Similarity\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14679/13761 Exploiting both Vertical and Horizontal Dimensions of Feature Hierarchy for Effective Recommendation\n",
      "http://aaai.org/ocs/index hghvcfdxnmjmnlk,jmhngc bgbgvfbvhjnkhyui65tf.php/AAAI/AAAI17/paper/view/14316/13762 Phrase-Based Presentation Slides Generation for Academic Papers\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14589/13763 Community Preserving Network Embedding\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14239/13764 CLARE: A Joint Approach to Label Classification and Tag Recommendation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14159/13765 Multiple Source Detection without Knowing the Underlying Propagation Model\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14506/13766 Learning Visual Sentiment Distributions via Augmented Conditional Probability Neural Network\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14964/13767 Visual Sentiment Analysis by Attending on Local Image Regions\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14360/13768 Correlated Cascades: Compete or Cooperate\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14349/13769 Finding Critical Users for Social Network Engagement: The Collapsed k-Core Problem\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14721/13770 Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15029/13771 Expectile Matrix Factorization for Skewed Data Analysis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14820/13772 Associative Memory Using Dictionary Learning and Expander Decoding\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14669/13773 An Integrated Model for Effective Saliency Prediction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14560/13774 The Efficiency of the HyperPlay Technique Over Random Sampling\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15015/13775 Market Pricing for Data Streams\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14541/13803 Automated Design of Robust Mechanisms\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14911/13776 Incentivising Monitoring in Open Normative Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14608/13780 Envy-Free Mechanisms with Minimum Number of Cuts\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14905/13781 Strategic Signaling and Free Information Disclosure in Auctions\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14623/13782 Complexity of Manipulating Sequential Allocation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14606/13783 Algorithms for Max-Min Share Fair Allocation of Indivisible Chores\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14903/13785 Nash Stability in Social Distance Games\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14895/13784 On Pareto Optimality in Social Distance Games\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14264/13786 Team-Maxmin Equilibrium: Efficiency Bounds and Algorithms\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15011/13787 A Study of Compact Reserve Pricing Languages\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14980/13777 Faster and Simpler Algorithm for Optimal Strategies of Blotto Game\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14283/13788 Preference Elicitation For Participatory Budgeting\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14605/13789 Exclusion Method for Finding Nash Equilibrium in Multiplayer Games\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14640/13790 Teams in Online Scheduling Polls: Game-Theoretic Aspects\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15040/13779 Probably Approximately Efficient Combinatorial Auctions via Machine Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14757/13791 Phragmén's Voting Methods and Justified Representation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14782/13792 Multiwinner Approval Rules as Apportionment Methods\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14855/13793 Dynamic Thresholding and Pruning for Regret Minimization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14621/13794 Optimizing Positional Scoring Rules for Rank Aggregation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14334/13795 On Markov Games Played by Bayesian and Boundedly-Rational Players\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14300/13796 Bounded Rationality of Restricted Turing Machines\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14894/13797 Winner Determination in Huge Elections with MapReduce\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14554/13798 Approximation and Parameterized Complexity of Minimax Approval Voting\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14266/13799 The Computational Complexity of Weighted Greedy Matching\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14537/13800 Disarmament Games\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14704/13801 The Complexity of Stable Matchings under Substitutable Preferences\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14667/13802 Small Representations of Big Kidney Exchange Graphs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14924/13778 What Do Multiwinner Voting Rules Do? An Experiment Over the Two-Dimensional Euclidean Domain\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14423/13804 Extensive-Form Perfect Equilibrium Computation in Two-Player Games\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14593/13805 Selfish Knapsack\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14635/13806 Obvious Strategyproofness Needs Monitoring for Good Approximations\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14675/13807 Crowdsourced Outcome Determination in Prediction Markets\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14942/13808 Security Games on a Plane\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14986/13809 Engineering Agreement: The Naming Game with Asymmetric and Heterogeneous Agents\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14631/13810 Vote Until Two of You Agree: Mechanisms with Small Distortion and Sample Complexity\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14607/13811 Computing Least Cores of Supermodular Cooperative Games\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14739/13812 Heuristic Search Value Iteration for One-Sided Partially Observable Stochastic Games\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14954/13813 Group Activity Selection on Social Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14965/13814 Resource Graph Games: A Compact Representation for Games with Structured Strategy Spaces\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14278/13815 Complexity of the Stable Invitations Problem\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14879/13816 Mechanism Design in Social Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14318/13817 Optimal Personalized Defense Strategy Against Man-In-The-Middle Attack\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14397/13818 Network,  Popularity and Social Cohesion: A Game-Theoretic Approach\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14970/13819 Sequential Peer Prediction: Learning to Elicit Effort using Posted Prices\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14410/13820 An Ambiguity Aversion Model for Decision Making under Ambiguity\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14562/13821 Optimal Pricing for Submodular Valuations with Bounded Curvature\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14511/13822 On Covering Codes and Upper Bounds for the Dimension of Simple Games\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14851/13823 Tractable Algorithms for Approximate Nash Equilibria in Generalized Graphical Games with Tree Structure\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14947/13824 Recognising Multidimensional Euclidean Preferences\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14945/13825 Preferences Single-Peaked on a Circle\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14925/13826 Psychological Forest: Predicting Human Behavior\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14871/13827 Revenue Maximization for Finitely Repeated Ad Auctions\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14917/13828 Proportional Justified Representation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14575/13829 Achieving Sustainable Cooperation in Generalized Prisoner's Dilemma with Observation Errors\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14801/13830 Mechanism Design for Multi-Type Housing Markets\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14770/13831 Constrained Pure Nash Equilibria in Polymatrix Games\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14602/13832 Axiomatic Characterization of Game-Theoretic Network Centralities\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14645/13833 Social Choice Under Metric Preferences: Scoring Rules and STV\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14578/13834 Fans Economy and All-Pay Auctions with Proportional Allocations\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14648/13835 The Positronic Economist: A Computational System for Analyzing Economic Mechanisms\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14375/13836 Non-Additive Security Games\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14414/13837 The Dollar Auction with Spiteful Players\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14642/13838 Proper Proxy Scoring Rules\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14713/13839 Randomized Mechanisms for Selling Reserved Instances in Cloud\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14398/13840 Embedded Bandits for Large-Scale Black-Box Optimization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14872/13841 Reactive Dialectic Search Portfolios for MaxSAT\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14750/13842 Efficient Parameter Importance Analysis via Ablation with Surrogates\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14789/13843 Problem Difficulty and the Phase Transition in Heuristic Search\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14489/13844 Automatic Logic-Based Benders Decomposition with MiniZinc\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14433/13845 Parallel Asynchronous Stochastic Variance Reduction for Nonconvex Optimization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14290/13846 A Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14617/13847 The Simultaneous Maze Solving Problem\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14272/13848 Going Beyond Primal Treewidth for (M)ILP\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14312/13849 Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14370/13850 An Exact Algorithm for the Maximum Weight Clique Problem in Large Graphs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14915/13851 Learning to Prune Dominated Action Sequences in Online Black-Box Planning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14797/13852 Systematic Exploration of Larger Local Search Neighborhoods for the Minimum Vertex Cover Problem\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14887/13853 New Lower Bound for the Minimum Sum Coloring Problem\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15021/13854 Anytime Anyspace AND/OR Search for Bounding the Partition Function\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14907/13855 Dancing with Decision Diagrams: A Combined Approach to Exact Cover\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14959/13856 Solving High-Dimensional Multi-Objective Optimization Problems with Low Effective Dimensions\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15034/13857 Automated Data Extraction Using Predictive Program Synthesis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15014/13858 Grid Pathfinding on the 2\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14483/13860 Non-Monotone DR-Submodular Function Maximization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14477/13859 Regret Ratio Minimization in Multi-Objective Submodular Function Maximization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15012/13861 Value Compression of Pattern Databases\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14500/13862 A Fast Algorithm to Compute Maximum\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14305/13863 A Unified Convex Surrogate for the Schatten-\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14373/13864 Efficient Stochastic Optimization for Low-Rank Distance Metric Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14773/13865 Examples-Rules Guided Deep Neural Network for Makeup Recommendation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14416/13866 Predicting Latent Narrative Mood Using Audio and Physiologic Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14840/13867 Collaborative Planning with Encoding of Users' High-Level Strategies\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14581/13868 Long-Term Trends in the Public Perception of Artificial Intelligence\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14804/13869 A Theoretical Analysis of First Heuristics of Crowdsourced Entity Resolution\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14353/13870 Pairwise HITS: Quality Estimation from Pairwise Comparisons in Creator-Evaluator Crowdsourcing Process\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14783/13871 The Benefit in Free Information Disclosure When Selling Information to People\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14795/13872 Psychologically Based Virtual-Suspect for Interrogative Interview Training\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14381/13873 PIVE: Per-Iteration Visualization Environment for Real-Time Interactions with Dimension Reduction and Clustering\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14786/13874 JAG: A Crowdsourcing Framework for Joint Assessment and Peer Grading\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15032/13875 On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14672/13876 Capturing Dependencies among Labels and Features for Multiple Emotion Tagging of Multimedia Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14358/13877 On the Computation of Paracoherent Answer Sets\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14163/13878 Polynomially Bounded Logic Programs with Function Symbols: A New Decidable\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14807/13879 Abstraction in Situation Calculus Action Theories\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14725/13880 Source Information Disclosure in Ontology-Based Data Integration\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14365/13881 Ontology-Mediated Queries for Probabilistic Databases\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14881/13882 Ontology-Based Data Access with a Horn Fragment of Metric Temporal Logic\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14369/13883 Solving Advanced Argumentation Problems with Answer-Set Programming\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14734/13884 Checking the Consistency of Combined Qualitative Constraint Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14627/13885 Add Data into Business Process Verification: Bridging the Gap between Theory and Practice\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14402/13886 Practical TBox Abduction Based on Justification Patterns\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14585/13887 The Unusual Suspects: Deep Learning Based Mining of Interesting Entity Trivia from Knowledge Graphs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14726/13888 Ontology Materialization by Abstraction Refinement in Horn SHOIF\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14357/13889 Number Restrictions on Transitive Roles in Description Logics with Nominals\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14209/13890 Strategic Sequences of Arguments for Persuasion Using Decision Trees\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14816/13891 Preferential Structures for Comparative Probabilistic Reasoning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14660/13892 Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14810/13893 Diagnosability Planning for Controllable Discrete Event Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14218/13895 Entropic Causal Inference\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14368/13896 SAT Encodings for Distance-Based Belief Merging Operators\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14547/13897 LPMLN,  Weak Constraints,  and P-log\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14268/13898 Graph-Based Wrong IsA Relation Detection in a Large-Scale Lexical Taxonomy\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14267/13899 On the Transitivity of Hypernym-Hyponym Relations in Data-Driven Lexical Taxonomies\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14172/13900 Don't Forget the Quantifiable Relationship between Words: Using Recurrent Neural Network for Short Text Topic Discovery\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14966/13901 The Symbolic Interior Point Method\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14516/13902 Small Is Beautiful: Computing Minimal Equivalent EL Concepts\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14919/13903 Compiling Graph Substructures into Sentential Decision Diagrams\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14194/13904 Efficient Evaluation of Answer Set Programs with External Sources Based on External Source Inlining\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14193/13905 On Equivalence and Inconsistency of Answer Set Programs with External Sources\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14279/13906 ProjE: Embedding Projection for Knowledge Graph Completion\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14524/13907 Non-Parametric Estimation of Multiple Embeddings for Link Prediction on Dynamic Knowledge Graphs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14923/13909 Causal Discovery Using Regression-Based Conditional Independence Tests\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15004/13908 An Improved Algorithm for Learning to Perform Exception-Tolerant Abduction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15047/13910 Trust-Sensitive Evolution of DL-Lite Knowledge Bases\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15003/13911 Explicit Defense Actions Against Test-Set Attacks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14466/13912 Multidimensional Scaling on Multiple Input Distance Matrices\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14337/13913 ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14715/13914 GLOMA: Embedding Global Information in Local Matrix Approximation Models for Collaborative Filtering\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14743/13915 Predicting Soccer Highlights from Spatio-Temporal Match Event Streams\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14676/13916 A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14438/13917 Collaborative Dynamic Sparse Topic Regression with User Profile Evolution for Item Recommendation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14421/13918 Event Video Mashup: From Hundreds of Videos to Minutes of Skeleton\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14448/13919 Soft Video Parsing by Label Distribution Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14165/13920 Active Learning with Cross-Class Similarity Transfer\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14603/13921 DeepFix: Fixing Common C Language Errors by Deep Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14572/13922 Question Difficulty Prediction for READING Problems in Standard Tests\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14521/13923 Additional Multi-Touch Attribution for Online Advertising\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14292/13924 Multitask Dyadic Prediction and Its Application in Prediction of Adverse Drug-Drug Interaction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14582/13925 Semi-Supervised Multi-View Correlation Feature Learning with Application to Webpage Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14543/13926 Contextual RNN-GANs for Abstract Reasoning Diagram Generation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14969/13927 A Framework for Minimal Clustering Modification via Constraint Programming\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14452/13928 Knowing What to Ask: A Bayesian Active Learning Approach to the Surveying Problem\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14625/13929 ERMMA: Expected Risk Minimization for Matrix Approximation-based Recommender Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14259/13930 Learning with Feature Network and Label Network Simultaneously\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14707/13931 Collaborative Company Profiling: Insights from an Employee's Perspective\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14817/13932 ESPACE: Accelerating Convolutional Neural Networks via Eliminating Spatial and Channel Redundancy\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14637/13933 A Sparse Dictionary Learning Framework to Discover Discriminative Source Activations in EEG Brain Mapping\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14385/13934 On Predictive Patent Valuation: Forecasting Patent Citations and Their Types\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14869/13935 Let Your Photos Talk: Generating Narrative Paragraph for Photo Stream via Bidirectional Attention Recurrent Neural Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14700/13936 Data-Driven Approximations to NP-Hard Problems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14818/13937 Predicting Demographics of High-Resolution Geographies with Geotagged Tweets\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14940/13938 Finding Cut from the Same Cloth: Cross Network Link Recommendation via Joint Matrix Factorization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14464/13944 FeaBoost: Joint Feature and Label Refinement for Semantic Segmentation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14989/13943 Learning Implicit Tasks for Patient-Specific Risk Modeling in ICU\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14765/13939 Enabling Dark Energy Science with Deep Generative Models of Galaxy Images\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14388/13940 Unsupervised Deep Learning for Optical Flow Estimation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14828/13941 Low-Rank Linear Cold-Start Recommendation from Social Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14830/13942 Exploring Normalization in Deep Residual Networks with Concatenated Rectified Linear Units\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14443/13945 Portfolio Selection via Subset Resampling\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14688/13946 Beyond IID: Learning to Combine Non-IID Metrics for Vision Tasks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14918/13947 Fast Inverse Reinforcement Learning with Interval Consistent Graph for Driving Behavior Prediction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14482/13948 Neural Programming by Example\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14333/13949 Simultaneous Clustering and Ensemble\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14630/13950 A Deep Hierarchical Approach to Lifelong Learning in Minecraft\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14260/13951 Learning Attributes from the Crowdsourced Relative Labels\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14440/13952 Coupling Implicit and Explicit Knowledge for Customer Volume Prediction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14691/13953 Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14570/13954 Multiset Feature Learning for Highly Imbalanced Data Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14282/13956 Adverse Drug Reaction Prediction with Symbolic Latent Dirichlet Allocation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14391/13955 Modeling the Intensity Function of Point Process Via Recurrent Neural Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14234/13957 Progressive Prediction of Student Performance in College Programs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14717/13958 Bridging Video Content and Comments: Synchronized Video Description with Temporal Summarization of Crowdsourced Time-Sync Comments\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14326/13959 Pairwise Relationship Guided Deep Hashing for Cross-Modal Retrieval\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14517/13960 Discriminative Semi-Supervised Dictionary Learning with Entropy Regularization for Pattern Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14890/13961 Fine-Grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14478/13962 Knowledge Transfer for Deep Reinforcement Learning with Hierarchical Experience Replay\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14156/13963 Personalized Donor-Recipient Matching for Organ Transplantation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14501/13964 Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14237/13965 Robust Manifold Matrix Factorization for Joint Clustering and Feature Extraction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14943/13966 Discrete Personalized Ranking for Fast Collaborative Filtering from Implicit Feedback\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14658/13967 Catch'Em All: Locating Multiple Diffusion Sources in Networks with Partial Observations\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14711/14322 Learning Bayesian Networks with Incomplete Data by Augmentation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14769/14323 Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15036/14324 Scalable Optimization of Multivariate Performance Measures in Multi-instance Multi-label Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14735/14325 The Bernstein Mechanism: Function Release under Differential Privacy\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14756/14326 Heavy-Tailed Analogues of the Covariance Matrix for ICA\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14538/14327 Fast Generalized Distillation for Semi-Supervised Domain Adaptation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14858/14328 The Option-Critic Architecture\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14814/14329 Label Efficient Learning by Exploiting Multi-Class Output Codes\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14992/14330 Robust Partially-Compressed Least-Squares\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14748/14331 Learning Residual Alternating Automata\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14860/14332 Resource Constrained Structured Prediction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14781/14333 Cross-Domain Kernel Induction for Transfer Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14329/14334 Informative Subspace Learning for Counterfactual Inference\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14335/14335 PAC Identification of a Bandit Arm Relative to a Reward Quantile\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14328/14336 Classification with Minimax Distance Measures\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14697/14337 Latent Discriminant Analysis with Representative Feature Discovery\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14230/14338 Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14201/14339 Sparse Boltzmann Machines with Structure Learning as Applied to Text Analysis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14846/14340 Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14378/14341 OFFER: Off-Environment Reinforcement Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14832/14342 Addressing Imbalance in Multi-Label Classification Using Structured Hellinger Forests\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14350/14343 Nonlinear Dynamic Boltzmann Machines for Time-Series Prediction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14740/14344 Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15002/14345 Scalable Multitask Policy Gradient Reinforcement Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14665/14346 From Shared Subspaces to Shared Landmarks: A Robust Multi-Source Classification Approach\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14486/14347 A Nearly-Black-Box Online Algorithm for Joint Parameter and State Estimation in Temporal Models\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14288/14348 Structure Regularized Unsupervised Discriminant Feature Analysis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14374/14349 Self-Paced Learning: An Implicit Regularization Perspective\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14747/14350 Deep MIML Network\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14802/14351 Modeling Skewed Class Distributions by Reshaping the Concept Space\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14480/14352 On Learning High Dimensional Structured Single Index Models\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15027/14353 Local Centroids Structured Non-Negative Matrix Factorization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14657/14354 Low-Rank Factorization of Determinantal Point Processes\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14759/14355 Robust Loss Functions under Label Noise for Deep Neural Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14205/14356 Exploring Commonality and Individuality for Multi-Modal Curriculum Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14429/14357 MPGL: An Efficient Matching Pursuit Method for Generalized LASSO\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14896/14358 Weighted Bandits or: How Bandits Learn Distorted Values That Are Not Expected\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14738/14359 Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14286/14360 Convex Co-Embedding for Matrix Completion with Predictive Side Information\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14777/14361 Continuous Conditional Dependency Network for Structured Regression\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14615/14362 Bilateral k-Means Algorithm for Fast Co-Clustering\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14784/14363 Alternating Back-Propagation for Generator Network\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14304/14364 Enumerate Lasso Solutions for Feature Selection\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14850/14365 Scalable Algorithm for Higher-Order Co-Clustering via Random Sampling\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14253/14366 Learning Invariant Deep Representation for NIR-VIS Face Recognition\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14200/14367 A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14436/14368 Semi-Supervised Adaptive Label Distribution Learning for Facial Age Estimation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14539/14369 Sampling Beats Fixed Estimate Predictors for Cloning Stochastic Behavior in Multiagent Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14929/14370 Sequential Classification-Based Optimization for Direct Policy Search\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14633/14371 A Riemannian Network for SPD Matrix Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14999/14372 Asynchronous Mini-Batch Gradient Descent with Variance Reduction for Non-Convex Optimization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14930/14373 Learning Unitary Operators with Help From u(n)\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14213/14374 Denoising Criterion for Variational Auto-Encoding Framework\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14990/14375 Recovering True Classifier Performance in Positive-Unlabeled Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14475/14376 Generalized Ambiguity Decompositions for Classification with Applications in Active Learning and Unsupervised Ensemble Pruning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14569/14377 Twin Learning for Similarity and Clustering: A Unified Kernel Approach\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14355/14378 Tunable Sensitivity to Large Errors in Neural Network Training\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14710/14379 Binary Embedding with Additive Homogeneous Kernels\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14215/14380 Structured Inference Networks for Nonlinear State Space Models\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15043/14381 Estimating Uncertainty Online Against an Adversary\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14465/14382 Learning Non-Linear Dynamics of Decision Boundaries for Maintaining Classification Performance\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14434/14383 Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14866/14384 Dynamic Action Repetition for Deep Reinforcement Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14456/14385 Playing FPS Games with Deep Reinforcement Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14315/14386 Transfer Reinforcement Learning with Shared Dynamics\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14803/14387 Transfer Learning for Deep Learning on Graph-Structured Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14343/14388 Efficient Online Model Adaptation by Incremental Simplex Tableau\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14534/14389 Multivariate Hawkes Processes for Large-Scale Inference\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14535/14390 Self-Paced Multi-Task Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14705/14391 Infinitely Many-Armed Bandits with Budget Constraints\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14226/14392 Sparse Subspace Clustering by Learning Approximation ℓ0 Codes\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14425/14393 Riemannian Submanifold Tracking on Low-Rank Algebraic Variety\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14686/14394 Large Graph Hashing with Spectral Rotation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14212/14395 Low-Rank Tensor Completion with Total Variation for Visual Data Inpainting\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14587/14396 Learning Safe Prediction for Semi-Supervised Regression\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14780/14397 A Two-Stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14693/14398 Balanced Clustering with Least Square Regression\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14813/14399 Ordinal Constrained Binary Code Learning for Nearest Neighbor Search\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14330/14400 Sparse Deep Transfer Learning for Convolutional Neural Network\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14175/14401 Cost-Sensitive Feature Selection via F-Measure Optimization Reduction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14758/14402 Multiple Kernel k-Means with Incomplete Kernels\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14761/14403 Optimal Neighborhood Kernel Clustering with Multiple Kernels\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14233/14404 Generalization Analysis for Ranking Using Integral Operator\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14186/14405 Infinite Kernel Learning: Generalization Bounds and Algorithms\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14526/14406 Accelerated Variance Reduced Stochastic ADMM\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15033/14407 Semi-Supervised Classifications via Elastic and Robust Embedding\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14853/14408 Approximate Conditional Gradient Descent on Multi-Class Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14469/14409 Probabilistic Non-Negative Matrix Factorization and Its Robust Extensions for Topic Modeling\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14632/14410 Active Search for Sparse Signals with Region Sensing\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15031/14411 Where to Add Actions in Human-in-the-Loop Reinforcement Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14835/14412 Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14868/14413 Generalization Error Bounds for Optimization Algorithms via Stability\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14849/14414 When and Why Are Deep Networks Better Than Shallow Ones?\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14505/14415 Lifted Inference for Convex Quadratic Programs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14530/14416 Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14650/14417 Deep Collective Inference\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14514/14419 Streaming Classification with Emerging New Class by Class Matrix Sketching\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14598/14418 Deep Hashing: A Joint Approach for Image Signature Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14584/14420 Tsallis Regularized Optimal Transport and Ecological Inference\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15020/14421 The Multivariate Generalised von Mises Distribution: Inference and Applications\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14364/14422 Querying Partially Labelled Data to Improve a K-nn Classifier\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14833/14423 Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15039/14424 Multiclass Capped ℓp-Norm SVM for Robust Classifications\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14404/14425 Unsupervised Large Graph Embedding\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14494/14426 Matching Node Embeddings for Graph Similarity\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14382/14427 Inductive Pairwise Ranking: Going Beyond the\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14952/14428 Active Search in Intensionally Specified Structured Spaces\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14383/14429 Top-k Hierarchical Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14325/14430 Unimodal Thompson Sampling for Graph-Structured Arms\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14460/14431 Accelerated Gradient Temporal Difference Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14473/14432 A General Framework for Sparsity Regularized Feature Selection via Iteratively Reweighted Least Square Minimization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14442/14433 Cascade Subspace Clustering\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14557/14434 Column Networks for Collective Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14812/14435 A General Clustering Agreement Index: For Comparing Disjoint and Overlapping Clusters\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14912/14436 Non-Negative Inductive Matrix Completion for Discrete Dyadic Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14599/14437 Online Active Linear Regression via Thresholding\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14678/14438 Adaptive Proximal Average Approximation for Composite Convex Minimization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14444/14439 Random Features for Shift-Invariant Kernels with Moment Matching\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14565/14440 Compressed K-Means for Large-Scale Clustering\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14674/14441 Patch Reordering: A NovelWay to Achieve Rotation and Translation Invariance in Convolutional Neural Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14467/14442 Asymmetric Discrete Graph Hashing\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14295/14443 Spectral Clustering with Brainstorming Process for Multi-View Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14616/14444 Parameter Free Large Margin Nearest Neighbor for Distance Metric Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14670/14445 Multilinear Regression for Embedded Feature Selection with Application to fMRI Analysis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14956/14446 Distributed Negative Sampling for Word Embeddings\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14967/14447 Label-Free Supervision of Neural Networks with Physics and Domain Knowledge\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14420/14448 Unsupervised Learning with Truncated Gaussian Graphical Models\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14961/14449 Automatic Curriculum Graph Generation for Reinforcement Learning Agents\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14551/14450 Self-Correcting Models for Model-Based Reinforcement Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14446/14451 Distant Domain Transfer Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14210/14452 Confidence-Rated Discriminative Partial Label Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14459/14453 Cross-Domain Ranking via Latent Space Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14619/14454 How to Train a Compact Binary Neural Network with High Accuracy?\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14838/14455 Policy Search with High-Dimensional Context Variables\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14938/14456 Coactive Critiquing: Elicitation of Preferences and Features\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14957/14457 Importance Sampling with Unequal Support\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14767/14458 Achieving Privacy in the Adversarial Multi-Armed Bandit\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14768/14459 Thompson Sampling for Stochastic Bandits with Graph Feedback\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14898/14460 Selecting Sequences of Items via Submodular Maximization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14737/14461 Variable Kernel Density Estimation in High-Dimensional Feature Spaces\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14284/14462 Regularization for Unsupervised Deep Neural Nets\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14346/14463 Relational Deep Learning: A Deep Latent Variable Model for Link Prediction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14976/14464 Factorization Bandits for Interactive Recommendation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14303/14465 Latent Smooth Skeleton Embedding\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15001/14466 Polynomial Optimization Methods for Matrix Factorization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14327/14467 Two-Dimensional PCA with F-Norm Minimization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14379/14468 Feature Selection Guided Auto-Encoder\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14427/14469 Fredholm Multiple Kernel Learning for Semi-Supervised Domain Adaptation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14683/14470 Fast Online Incremental Learning on Mixture Streaming Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14998/14482 Efficient Ordered Combinatorial Semi-Bandits for Whole-Page Recommendation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14214/14472 Unbiased Multivariate Correlation Analysis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14291/14473 Beyond RPCA: Flattening Complex Noise in the Frequency Domain\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14241/14474 Improving Efficiency of SVM\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14307/14475 Rank Ordering Constraints Elimination with Application for Kernel Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14744/14476 Solving Indefinite Kernel Support Vector Machine with Difference of Convex Functions Programming\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14971/14477 Cleaning the Null Space: A Privacy Mechanism for Predictors\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14532/14478 Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14702/14479 A General Efficient Hyperparameter-Free Algorithm for Convolutional Sparse Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14439/14480 Multi-View Correlated Feature Learning by Uncovering Shared Component\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14487/14481 A Framework of Online Learning with Imbalanced Streaming Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14626/14485 TaGiTeD: Predictive Task Guided Tensor Decomposition for Representation Learning from Electronic Health Records\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14586/14486 Deep Learning for Fixed Model Reuse\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14166/14487 Learning Deep Latent Space for Multi-Label Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14620/14488 A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14344/14489 SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14742/14490 CBRAP: Contextual Bandits with RAndom Projection\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14310/14491 An Exact Penalty Method for Binary Optimization Based on MPEC Formulation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14914/14484 Scalable Feature Selection via Distributed Diversity Maximization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14687/14492 Fast Compressive Phase Retrieval under Bounded Noise\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14590/14493 Query-Efficient Imitation Learning for End-to-End Simulated Driving\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14909/14494 Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14157/14495 Universum Prescription: Regularization Using Unlabeled Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14430/14496 Learning Sparse Task Relations in Multi-Task Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14647/14497 Multi-View Clustering via Deep Matrix Factorization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14877/14498 SCOPE: Scalable Composite Optimization for Learning on Spark\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14892/14499 Lock-Free Optimization for Non-Convex Problems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14696/14500 Scalable Graph Embedding for Asymmetric Proximity\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14426/14501 Bilinear Probabilistic Canonical Correlation Analysis via Hybrid Concatenations\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14225/14502 Parametric Dual Maximization for Non-Convex Learning Problems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14221/14503 One-Step Spectral Clustering via Dynamically Learning Affinity Matrix and Subspace\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14498/14483 Multi-Kernel Low-Rank Dictionary Pair Learning for Multiple Features Based Image Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14492/14504 Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14882/14068 Improving Surveillance Using Cooperative Target Observation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14180/14069 Query Complexity of Tournament Solutions\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14545/14070 Centralized versus Personalized Commitments and Their Influence on Cooperation in Group Interactions\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14841/14071 Kont: Computing Tradeoffs in Normative Multiagent Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14981/14072 Parameterised Verification of Infinite State Multi-Agent Systems via Predicate Abstraction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14928/14073 Decentralized Planning in Stochastic Environments with Submodular Rewards\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14208/14074 Solving Seven Open Problems of Offline and Online Control in Borda Elections\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14891/14075 Collective Multiagent Sequential Decision Making Under Uncertainty\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14662/14076 Nurturing Group-Beneficial Information-Gathering Behaviors Through Above-Threshold Criteria Setting\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14525/14077 Improving Multi-Document Summarization via Text Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14491/14078 Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14513/14079 Neural Bag-of-Ngrams\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14636/14080 SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14764/14081 Unit Dependency Graph and Its Application to Arithmetic Word Problem Solving\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14614/14082 Prerequisite Skills for Reading Comprehension: Multi-Perspective Analysis of MCTest Datasets and Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14161/14083 Neural Machine Translation with Reconstruction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14306/14084 SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14576/14085 Efficiently Answering Technical Questions — A Knowledge Graph Approach\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14170/14086 Incorporating Knowledge Graph Embeddings into Topic Modeling\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14822/14087 A Context-Enriched Neural Network Method for Recognizing Lexical Entailment\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14653/14184 Bayesian Neural Word Embedding\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14724/14187 Improving Word Embeddings with Convolutional Feature Learning and Subword Information\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14527/14185 Joint Copying and Restricted Generation for Paraphrase\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14564/14188 Unsupervised Learning of Evolving Relationships Between Literary Characters\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14495/14190 Translation Prediction with Source Dependency-Based Context Representation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14394/14192 Maximum Reconstruction Estimation for Generative Latent-Variable Models\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14628/14193 Incorporating Expert Knowledge into Keyphrase Extraction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14276/14195 Unsupervised Learning for Lexicon-Based Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14542/14196 Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14699/14198 Geometry of Compositionality\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14428/14199 Disambiguating Spatial Prepositions Using Deep Convolutional Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14484/14201 A Unified Model for Cross-Domain and Semi-Supervised Named Entity Recognition in Chinese Social Media\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14400/14203 Recurrent Attentional Topic Model\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14939/14205 Representations of Context in Recognizing the Figurative and Literal Usages of Idioms\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14317/14206 Deterministic Attention for Sequence-to-Sequence Constituent Parsing\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14692/14208 S2JSD-LSH: A Locality-Sensitive Hashing Schema for Probability Distributions\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14164/14210 Coherent Dialogue with Attention-Based Language Models\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14827/14211 Definition Modeling: Learning to Define Word Embeddings in Natural Language\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14732/14213 Incrementally Learning the Hierarchical Softmax Function for Neural Language Models\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14223/14214 Condensed Memory Networks for Clinical Diagnostic Inferencing\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14332/14216 Robsut Wrod Reocginiton via Semi-Character Recurrent Neural Network\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14571/14217 Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14567/14219 A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14320/14220 Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14843/14221 Semantic Parsing with Neural Hybrid Trees\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14441/14256 Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14701/14255 Dual-Clustering Maximum Entropy with Application to Classification and Word Embedding\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14451/14257 Neural Machine Translation Advised by Statistical Machine Translation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14723/14258 A Dynamic Window Neural Network for CCG Supertagging\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14177/14259 Distinguish Polarity in Bag-of-Words Visualization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14563/14260 Topic Aware Neural Response Generation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14299/14261 Variational Autoencoder for Semi-Supervised Text Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14776/14262 Neural Models for Sequence Chunking\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14322/14263 BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14682/14264 Bilingual Lexicon Induction from Non-Parallel Data with Minimal Supervision\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14174/14265 Active Discriminative Text Representation Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14601/14266 Learning Context-Specific Word/Character Embeddings\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14471/14267 Mechanism-Aware Neural Machine for Dialogue Response Generation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14412/14125 Bootstrapping Distantly Supervised IE Using Joint Learning and Small Well-Structured Corpora\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14904/14126 Using Discourse Signals for Robust Instructor Intervention Prediction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14779/14127 Automatic Emphatic Information Extraction from Aligned Acoustic Data and Its Application on Sentence Compression\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14656/14129 Unsupervised Sentiment Analysis with Signed Social Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14865/14130 Recurrent Neural Networks with Auxiliary Labels for Cross-Domain Opinion Target Extraction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14472/14131 Distant Supervision via Prototype-Based Global Representation Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14324/14132 What Happens Next? Future Subevent Prediction Using Contextual Hierarchical LSTM\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14741/14133 Efficient Dependency-Guided Named Entity Recognition\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14502/14134 Improving Event Causality Recognition with Multiple Background Knowledge Sources Using Multi-Column Convolutional Neural Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14823/14135 Efficiently Mining High Quality Phrases from Texts\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14931/14137 Learning Latent Sentiment Scopes for Entity-Level Sentiment Analysis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14265/14139 Structural Correspondence Learning for Cross-Lingual Sentiment Classification with One-to-Many Mappings\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14613/14140 Salience Estimation via Variational Auto-Encoders for Multi-Document Summarization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14792/14141 Collaborative User Clustering for Short Text Streams\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14384/14142 Word Embedding Based Correlation Model for Question/Answer Matching\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14861/14143 Greedy Flipping for Constrained Word Deletion\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14611/14144 Attentive Interactive Neural Networks for Answer Selection in Community Question Answering\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14207/14147 Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14808/14151 Plan Reordering and Parallel Execution — A Parameterized Complexity View\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14311/14152 Validating Domains and Plans for Temporal Planning via Encoding into Infinite-State Linear Temporal Logic\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14652/14154 On the Disruptive Effectiveness of Automated Planning for LTL\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15009/14155 Bounding the Probability of Resource Constraint Violations in Multi-Agent MDPs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14819/14157 Optimizing Quantiles in Preference-Based Markov Decision Processes\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14886/14158 An Analysis of Monte Carlo Tree Search\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14771/14160 An Efficient Approach to Model-Based Hierarchical Reinforcement Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14862/14161 Best-First Width Search: Exploration and Exploitation in Classical Planning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14641/14163 Robust Execution of Probabilistic Temporal Plans\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14984/14164 Multi-Agent Path Finding with Delay Probabilities\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14958/14166 Logical Filtering and Smoothing: State Estimation in Partially Observable Domains\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14666/14167 Landmark-Based Heuristics for Goal Recognition\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14991/14169 Fast SSP Solvers Using Short-Sighted Labeling\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14766/14170 Higher-Dimensional Potential Heuristics for Optimal Classical Planning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14955/14172 Schematic Invariants by Reduction to Ground Invariants\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14762/14173 Narrowing the Gap Between Saturated and Optimal Cost Partitioning for Classical Planning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14979/14175 Incorporating Domain-Independent Planning Heuristics in Hierarchical Planning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14203/14176 Computational Issues in Time-Inconsistent Planning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14960/14178 Accelerated Vector Pruning for Optimal POMDP Solvers\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14978/14179 When to Reset Your Keys: Optimal Timing of Security Updates via Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14936/14180 Human-Aware Plan Recognition\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14309/14088 Minimal Undefinedness for Fuzzy Answer Sets\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15008/14089 Open-Universe Weighted Model Counting\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14889/14090 Deterministic versus Probabilistic Methods for Searching for an Evasive Target\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15026/14091 Non-Deterministic Planning with Temporally Extended Goals: LTL over Finite and Infinite Traces\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14354/14092 Optimizing Expectation with Guarantees in POMDPs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14411/14093 Latent Dependency Forest Models\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15037/14094 Causal Effect Identification by Adjustment under Confounding and Selection Biases\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15035/14095 The Linearization of Belief Propagation on Pairwise Markov Random Fields\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14618/14096 The Kernel Kalman Rule — Efficient Nonparametric Inference with Recursive Least Squares\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14754/14097 Misspecified Linear Bandits\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14566/14098 Reasoning about Cognitive Trust in Stochastic Multiagent Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14908/14099 Anytime Best+Depth-First Search for Bounding Marginal MAP\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14774/14100 Multi-Objective Influence Diagrams with Possibly Optimal Policies\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14946/14101 Hindsight Optimization for Hybrid State and Action MDPs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14497/14102 I See What You See: Inferring Sensor and Policy Models of Human Real-World Motor Behavior\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14450/14103 Solving Constrained Combinatorial Optimisation Problems via MAP Inference without High-Order Penalties\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14622/14104 Deep Learning Quadcopter Control via Risk-Aware Active Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14937/14105 Latent Dirichlet Allocation for Unsupervised Activity Analysis on an Autonomous Mobile Robot\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14419/14106 Unsupervised Feature Learning for 3D Scene Reconstruction with Occupancy Maps\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14993/14107 Grounded Action Transformation for Robot Learning in Simulation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14363/14108 A Diversified Generative Latent Variable Model for WiFi-SLAM\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14509/14109 Associate Latent Encodings in Learning from Demonstrations\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14834/14110 Dynamically Constructed (PO)MDPs for Adaptive Robot Planning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14380/14111 A SAT-Based Approach for Solving the Modal Logic S5-Satisfiability Problem\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14274/14112 A BTP-Based Family of Variable Elimination Rules for Binary  CSPs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14515/14113 Algorithms for Deciding Counting Quantifiers over Unary Predicates\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14968/14114 Maximum Model Counting\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14755/14123 Phase Transitions for Scale-Free SAT Formulas\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14815/14115 The Opacity of Backbones\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14948/14116 Between Subgraph Isomorphism and Maximum Common Subgraph\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14873/14117 Should Algorithms for Random SAT and Max-SAT Be Different?\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14544/14118 Soft and Cost MDD Propagators\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14706/14119 Rigging Nearly Acyclic Tournaments Is Fixed-Parameter Tractable\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14638/14120 RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14191/14121 CoCoA: A Non-Iterative Approach to a Local Search (A)DCOP Solver\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14359/14122 Extending Compact-Table to Negative and Short Tables\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14874/14124 General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14463/14268 Regularized Diffusion Process for Visual Retrieval\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14499/14269 Collective Deep Quantization for Efficient Cross-Modal Retrieval\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14249/14270 Reference Based LSTM for Image Captioning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14313/14271 A Multi-Task Deep Network for Person Re-Identification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14462/14272 VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14431/14273 Deep Correlated Metric Learning for Sketch-based 3D Shape Retrieval\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14825/14321 Deep Manifold Learning of Symmetric Positive Definite Matrices with Application to Face Recognition\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14457/14275 Sherlock: Scalable Fact Learning in Images\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14351/14276 Robust Visual Tracking via Local-Global Correlation Filter\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14624/14277 DECK: Discovering Event Composition Knowledge from Web Images for Zero-Shot Event Detection and Recounting in Videos\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14673/14278 Differentiating Between Posed and Spontaneous Expressions with Latent Regression Bayesian Network\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14856/14279 Active Video Summarization: Customized Summaries via On-line Interaction with the User\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14733/14280 Building an End-to-End Spatial-Temporal Convolutional Network for Video Super-Resolution\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14160/14281 Zero-Shot Recognition via Direct Classifier Learning with Transferred Samples and Pseudo Labels\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14749/14282 Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14629/14283 Weakly Supervised Learning of Part Selection Model with Spatial Constraints for Fine-Grained Image Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15019/14284 Video Recovery via Learning Variation and Consistency of Images\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14405/14285 Nonnegative Orthogonal Graph Matching\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14294/14286 Multi-Path Feedback Recurrent Neural Networks for Scene Parsing\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14154/14287 Detection and Recognition of Text Embedded in Online Images via Neural Context Models\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14445/14288 Weakly Supervised Semantic Segmentation Using Superpixel Pooling Network\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14512/14289 Robust MIL-Based Feature Template Learning for Object Tracking\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14301/14290 Learning Patch-Based Dynamic Graph for Visual Tracking\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14880/14291 Image Caption with Global-Local Attention\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14338/14292 Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14387/14293 A Multiview-Based Parameter Free Framework for Group Detection\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14169/14294 Weakly-Supervised Deep Nonnegative Low-Rank Model for Social Image Tag Refinement and Assignment\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14202/14295 TextBoxes: A Fast Text Detector with a Single Deep Neural Network\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14751/14296 An Artificial Agent for Robust Image Registration\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14246/14297 Attention Correctness in Neural Image Captioning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14336/14298 Boosting Complementary Hash Tables for Fast Nearest Neighbor Search\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14323/14299 Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14926/14300 Video Captioning with Listwise Supervision\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14510/14301 Closing the Loop for Edge Detection and Object Proposals\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14708/14302 Learning Discriminative Activated Simplices for Action Recognition\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14188/14303 Non-Rigid Point Set Registration with Robust Transformation Estimation under Manifold Regularization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14184/14304 Online Multi-Target Tracking Using Recurrent Neural Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14888/14305 Text-Guided Attention Model for Image Captioning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14488/14306 Fully Convolutional Neural Networks with Full-Scale-Features for Semantic Segmentation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15005/14307 Title Learning Latent Subevents in Activity Videos Using Temporal Attention Filters\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14847/14308 Privacy-Preserving Human Activity Recognition from Extreme Low Resolution\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14437/14309 An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14695/14310 Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14806/14311 Inception-v4,  Inception-ResNet and the Impact of Residual Connections on Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14331/14312 Image Cosegmentation via Saliency-Guided Constrained Clustering with Cosine Similarity\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14393/14313 Quantifying and Detecting Collective Motion by Manifold Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14155/14314 Cross-View People Tracking by Scene-Centered Spatio-Temporal Parsing\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14644/14315 Unsupervised Learning of Multi-Level Descriptors for Person Re-Identification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14857/14316 Leveraging Saccades to Learn Smooth Pursuit: A Self-Organizing Motion Tracking Model Using Restricted Boltzmann Machines\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14162/14317 Efficient Object Instance Search Using Fuzzy Objects Matching\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14340/14318 Face Hallucination with Tiny Unaligned Images by Transformative Discriminative Neural Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14906/14319 Leveraging Video Descriptions to Learn Video Question Answering\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14519/14320 Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14913/14038 Natural Language Acquisition and Grounding for Embodied Robotic Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14548/14039 Analogical Chaining with Natural Language Instruction for Commonsense Reasoning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14916/14040 Inductive Reasoning about Ontologies Using Conceptual Spaces\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14864/14041 Integrating the Cognitive with the Physical: Musical Path Planning for an Improvising Robot\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14811/14042 Imagined Visual Representations as Multimodal Embeddings\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14933/14043 Goal Operations for Cognitive Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14507/14044 Combining Logical Abduction and Statistical Induction: Discovering Written Primitives with Human Knowledge\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14372/14045 Reactive Versus Anticipative Decision Making in a Novel Gift-Giving Game\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14839/14046 Towards Continuous Scientific Data Analysis and Hypothesis Evolution\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15044/14047 Flexible Model Induction through Heuristic Process Discovery\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14837/14048 When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems?\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14867/14049 Scanpath Complexity: Modeling Reading Effort Using Gaze Information\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14876/14050 Identifying Useful Inference Paths in Large Commonsense Knowledge Bases by Retrograde Analysis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14972/14051 ConceptNet 5.5: An Open Multilingual Graph of General Knowledge\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14596/14052 Towards a Brain Inspired Model of Self-Awareness for Sociable Agents\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14997/14053 Semantic Proto-Role Labeling\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14885/14054 Matrix Factorisation for Scalable Energy Breakdown\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14173/14055 Regularization in Hierarchical Time Series Forecasting with Application to Electricity Smart Meter Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14308/14056 Maximizing the Probability of Arriving on Time: A Practical Q-Learning Method\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14870/14057 Counting-Based Reliability Estimation for Power-Transmission Grids\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14248/14058 Three New Algorithms to Solve N-POMDPs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14583/14059 Fine-Grained Car Detection for Visual Census Estimation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15018/14060 Spatial Projection of Multiple Climate Variables Using Hierarchical Multitask Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14859/14061 Species Distribution Modeling of Citizen Science Data as a Classification Problem with Class-Conditional Noise\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14181/14062 Combining Satellite Imagery and Open Data to Map Road Safety\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14552/14063 Fast-Tracking Stationary MOMDPs for Adaptive Management Problems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14424/14064 Extracting Urban Microclimates from Electricity Bills\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14821/14065 Robust Optimization for Tree-Structured Stochastic Network Design\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14805/14066 Dynamic Optimization of Landscape Connectivity Embedding Spatial-Capture-Recapture Information\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14435/14067 Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14216/13968 Healthy Cognitive Aging: A Hybrid Random Vector Functional-Link Model for the Analysis of Alzheimer's Disease\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14302/13969 Mixed Discrete-Continuous Planning with Convex Optimization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14983/13970 Integration of Planning with Recognition for Responsive Interaction Using Classical Planners\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14609/13971 Configuration Planning with Temporal Constraints\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14270/13972 Learning to Predict Intent from Gaze During Robotic Hand-Eye Coordination\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14973/13973 Vision-Language Fusion for Object Recognition\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14277/13974 State Projection via AI Planning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14261/13975 Building Task-Oriented Dialogue Systems for Online Shopping\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14922/13714 Large-Scale Occupational Skills Normalization for Online Recruitment\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14799/13715 Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14275/13716 A Machine Learning Approach for Semantic Structuring of Scientific Charts in Scholarly Documents\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14604/13717 ParkUs: A Novel Vehicle Parking Detection System\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14206/13718 UbuntuWorld 1.0 LTS — A Platform for Automated Problem Solving & Troubleshooting in the Ubuntu OS\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14204/13719 Calories Prediction from Food Images\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/15010/13720 Real-Time Indoor Localization in Smart Homes Using Semi-Supervised Learning\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14432/13721 Constraint-Based Verification of a Mobile App Game Designed for Nudging People to Attend Cancer Screening\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14591/13722 Predicting Fuel Consumption and Flight Delays for Low-Cost Airlines\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14356/13723 Cracks Under Pressure? Burst Prediction in Water Networks Using Dynamic Metrics\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14490/13724 Determining Relative Airport Threats from News and Social Media\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14219/13725 Risk-Aware Planning: Methods and Case Study for Safer Driving Routes\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14975/13726 Designing Better Playlists with Monte Carlo Tree Search\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14963/13727 On Designing a Social Coach to Promote Regular Aerobic Exercise\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14171/13728 Crowdsensing Air Quality with Camera-Enabled Mobile Devices\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14577/13729 Optimal Sequential Drilling for Hydrocarbon Field Development Planning\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14550/13730 Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems,  with Applications to Digital Marketing\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/15007/13731 Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset.\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14974/13732 A Logic Based Approach to Answering Questions about Alternatives in DIY Domains\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/14236/13733 Automated Data Cleansing through Meta-Learning\n",
      "http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/15046/13734 Explainable Agency for Intelligent Autonomous Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14996/13976 ARTY: Fueling Creativity through Art,  Robotics and Technology for Youth\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14281/13977 Cornhole: A Widely-Accessible AI Robotics Task\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14555/13978 A Summer Research Experience in Robotics\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14546/13979 Creating Serious Robots That Improve Society\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14654/13980 Recovering Concept Prerequisite Relations from University Course Dependencies\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14224/13981 Open-Ended Robotics Exploration Projects for Budding Researchers\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14646/13982 Dude,  Where's My Robot?:  A Localization Challenge for Undergraduate Robotics\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15025/13983 A Monte Carlo Localization Assignment Using a Neato Vacuum with ROS\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14893/13984 An Image Wherever You Look! Making Vision Just Another Sensor for AI/Robotics Projects\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15023/13985 Application for AI-OCR Module: Auto Detection of Emails/Letter Images\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15028/13986 Exploring Artificial Intelligence Through Image Recognition\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14401/13987 Online SPARC for Drawing and Animation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14826/13988 AI Projects for Computer Science Capstone Classes (Extended Abstract)\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15013/13989 Model AI Assignments 2017\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14775/13990 The AI Rebellion: Changing the Narrative\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14651/13991 Moral Decision Making Frameworks for Artificial Intelligence\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14271/13992 Why Teaching Ethics to AI Practitioners Is Important\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14941/13993 Strategic Social Network Analysis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14982/13994 Getting More Out of the Exposed Structure in Constraint Programming Models of Combinatorial Problems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14994/13995 A Selected Summary of AI for Computational Sustainability\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14988/13996 Explaining Ourselves: Human-Aware Constraint Reasoning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14949/13997 Multi-Robot Allocation of Tasks with Temporal and Ordering  Constraints\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15042/13998 Progress and Challenges in Research on Cognitive Architectures\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14995/13999 Machine Learning for Entity Coreference Resolution: A Retrospective Look at Two Decades of Research\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14950/144000 Incidental Supervision: Moving beyond Supervised Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14493/14001 Latent Tree Analysis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14367/14136 Improving Performance of Analogue Readout Layers for Photonic Reservoir Computers with Online Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14366/14138 Chaotic Time Series Prediction Using a Photonic Reservoir Computer with Output Feedback\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14531/14145 Improving Greedy Best-First Search by Removing Unintended Search Bias (Extended Abstract)\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14897/14146 Frame-Based Ontology Alignment\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14727/14148 Learning Options in Multiobjective Reinforcement Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14731/14149 Towards User Personality Profiling from Multiple Social Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14256/14150 Semantic Inference of Bird Songs Using Dynamic Bayesian Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14413/14153 An Advising Framework for Multiagent Reinforcement Learning Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14760/14156 Android Malware Detection with Weak Ground Truth Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14800/14159 Discovering Conversational Dependencies between Messages in Dialogs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14371/14162 Coordinating Human and Agent Behavior in Collective-Risk Scenarios\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14962/14165 The Complexity of Succinct Elections\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14377/14168 A Position-Biased PageRank Algorithm for Keyphrase Extraction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14785/14171 Robust Stable Marriage\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14176/14174 Handwriting Profiling Using Generative Adversarial Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14504/14177 Policy Reuse in Deep Reinforcement Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15017/14181 Grounded Action Transformation for Robot Learning in Simulation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15006/14182 Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14470/14183 Fast Electrical Demand Optimization Under Real-Time Pricing\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14262/14186 SReN: Shape Regression Network for Comic Storyboard Extraction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14580/14189 Cross-Domain Sentiment Classification via Topic-Related TrAdaBoost\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14485/14191 A Deep Learning Approach for Arabic Caption Generation Using Roots-Words\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14920/14194 Learning to Avoid Dominated Action Sequences in Planning for Black-Box Domains\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14714/14197 Kernelized Evolutionary Distance Metric Learning for Semi-Supervised Clustering\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14549/14200 Redesigning Stochastic Environments for Maximized Utility\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14185/14202 Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14927/14204 Wikitop: Using Wikipedia Category Network to Generate Topic Trees\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14798/14207 Predicting Mortality of Intensive Care Patients via Learning about Hazard\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14573/14209 Rethinking the Link Prediction Problem in Signed Social Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14319/14212 ATSUM: Extracting Attractive Summaries for News Propagation on Microblogs\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14197/14215 A Systematic Practice of Judging the Success of a Robotic Grasp Using Convolutional Neural Network\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14403/14218 Neuron Learning Machine for Representation Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14211/14222 Community-Based Question Answering via Contextual Ranking Metric Network Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14579/14223 Auto-Annotation of 3D Objects via ImageNet\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14352/14224 Semantic Interpretation of Social Network Communities\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14934/14225 Extreme Gradient Boosting and Behavioral Biometrics\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14528/14226 Plan Recognition Design\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14899/14227 Automatically Extracting Axioms in Classical Planning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14729/14228 SEAPoT-RL: Selective Exploration Algorithm  for Policy Transfer in RL\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14836/14229 Coalition Structure Generation Utilizing Graphical Representation of Partition Function Games\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14235/14231 Audio Feature Learning with Triplet-Based Embedding Network\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14883/14232 A Finite Memory Automaton for Two-Armed Bernoulli Bandit Problems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14574/14233 Semantic Representation Using Explicit Concept Space Models\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14595/14234 A Sampling Based Approach for Proactive Project Scheduling with Time-Dependent Duration Uncertainty\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14951/14235 PAG2ADMG: A Novel Methodology to Enumerate Causal Graph Structures\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14540/14236 Preference Elicitation in DCOPs for Scheduling Devices in Smart Buildings\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14831/14237 Multimodal Fusion of EEG and Musical Features in Music-Emotion Recognition\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14228/14238 Predicting User Roles from Computer Logs Using Recurrent Neural Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14342/14239 Extracting Highly Effective Features for Supervised Learning via Simultaneous Tensor Factorization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14788/14240 Hybridizing Interval Temporal Logics: The First Step\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14852/14241 Boosting for Real-Time Multivariate Time Series Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14399/14242 Semantic Connection Based Topic Evolution\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14298/14243 Keyphrase Extraction with Sequential Pattern Mining\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14655/14244 Cycle-Based Singleton Local Consistencies\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14790/14245 Evolutionary Machine Learning for RTS Game StarCraft\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14418/14246 Enhancing the Privacy of Predictors\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14153/14247 Detecting Review Spammer Groups\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14151/14248 Attention Based LSTM for Target Dependent Sentiment Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14152/14249 Authorship Attribution with Topic Drift Model\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14240/14250 Participatory Art Museum: Collecting and Modeling Crowd Opinions\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14453/14251 High-Resolution Mobile Fingerprint Matching via Deep Joint KNN-Triplet Embedding\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14232/14252 A Computational Assessment Model for the Adaptive Level of Rehabilitation Exergames for the Elderly\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14395/14253 Natural Language Person Retrieval\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14220/14254 User Modeling Using LSTM Networks\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14247/14002 Explainable Image Understanding Using Vision and Reasoning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14376/14003 Problem Formulation for Accommodation Support in Plan-Based Interactive Narratives\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14671/14004 An Evolutionary Algorithm Based Framework for Task Allocation in Multi-Robot Teams\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14217/14005 Accelerating Multiagent Reinforcement Learning through Transfer Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14787/14006 Improving Deep Reinforcement Learning with Knowledge Transfer\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14167/14007 Problems in Large-Scale Image Classification\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14461/14008 Representations for Continuous Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14568/14009 Structured Prediction in Time Series Data\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15016/14010 A Supervised Sparse Learning Framework to Solve EEG Inverse Problem for Discriminative Activations Pattern\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14190/14011 V for Verification: Intelligent Algorithm of Checking Reliability of Smart Systems\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14447/14012 Modelling Familiarity for Intelligent Personalized Social Mobilization\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14698/14013 Transfer of Knowledge through Collective Learning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14588/14014 Project Scheduling in Complex Business Environments\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14793/14015 Human-Like Spatial Reasoning Formalisms\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14520/14016 Joint Learning of Structural and Textual Features for Web Scale Event Extraction\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14458/14017 Scalable Nonparametric Tensor Analysis\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14977/14018 SAT Competition 2016: Recent Developments\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14809/14019 What's Hot in Evolutionary Computation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15041/14020 What's Hot in Case-Based Reasoning\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14745/14021 Automated Negotiating Agents Competition (ANAC)\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14752/14022 What's Hot in Constraint Programming\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14842/14023 What's Hot at CPAIOR (Extended Abstract)\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/15030/14024 The State of the AIIDE Conference in 2017\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14875/14025 Deep Music: Towards Musical Dialogue\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14182/14026 AniDraw: When Music and Dance Meet Harmoniously\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14455/14027 Arnold: An Autonomous Agent to Play FPS Games\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14523/14028 A Virtual Personal Fashion Consultant: Learning from the Personal Preference of Fashion\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14794/14029 Efficient Clinical Concept Extraction in Electronic Medical Records\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14592/14030 Integrating Verbal and Nonvebval Input into a Dynamic Response Spoken Dialogue System\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14533/14031 Visual Memory QA: Your Personal Photo and Video Search Agent\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14179/14032 Sarcasm Suite: A Browser-Based Engine for Sarcasm Detection and Generation\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14659/14033 An Event Reconstruction Tool for Conflict Monitoring Using Social Media\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14661/14034 Webly-Supervised Learning of Multimodal Video Detectors\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14522/14035 SenseRun: Real-Time Running Routes Recommendation towards Providing Pleasant Running Experiences\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14694/14036 Natural Language Dialogue for Building and Learning Models and Structures\n",
      "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14600/14037 From Semantic Models to Cognitive Buildings\n",
      " len: 785\n"
     ]
    }
   ],
   "source": [
    "import requests, json\n",
    "from lxml import etree \n",
    "\n",
    "proxies = { \"http\": \"http://127.0.0.1:1081\", \"https\": \"http://127.0.0.1:1081\", } \n",
    "year = 17\n",
    "headers={\n",
    "    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n",
    "    'Accept-Encoding': 'gzip, deflate, br',\n",
    "    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',\n",
    "    'Cache-Control': 'max-age=0',\n",
    "    'Connection': 'keep-alive',\n",
    "    'DNT': '1',\n",
    "    'Host': 'www.aaai.org',\n",
    "    'Referer': 'https://www.aaai.org/Library/conferences-library.php',\n",
    "    'Sec-Fetch-Mode': 'navigate',\n",
    "    'Sec-Fetch-Site': 'same-origin',\n",
    "    'Sec-Fetch-User': '?1',\n",
    "    'Upgrade-Insecure-Requests': '1',\n",
    "    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.117 Safari/537.36',\n",
    "    }\n",
    "url = f'https://www.aaai.org/Library/AAAI/aaai{year}contents.php'\n",
    "with requests.Session() as s:\n",
    "    response = s.get(url, headers = headers, proxies=proxies)\n",
    "    print(f'{year} status code: {response.status_code}',)\n",
    "pdf_urls_dict = {}\n",
    "res_text = response.text.replace('<!-- ','').replace(' -->','')\n",
    "for node in etree.HTML(res_text).xpath('//a[.=\"PDF\"]/..'):\n",
    "    url = node.xpath('a[.=\"PDF\"]/@href')[0].strip()\n",
    "    name = node.xpath('a[1]/text()')[0].strip()\n",
    "    print(url, name)\n",
    "    pdf_urls_dict[name] = url\n",
    "print (f' len: {len(pdf_urls_dict.items())}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from multiprocessing.dummy import Pool\n",
    "from tqdm import tqdm\n",
    "import requests, json, os, re, time\n",
    "from PyPDF4 import PdfFileReader\n",
    "from configs import get_headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "headers2 = {\n",
    "'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n",
    "'Accept-Encoding': 'gzip, deflate, br',\n",
    "'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',\n",
    "'Connection': 'keep-alive',\n",
    "'Cookie': 'OCSSID=og0th8hnn4hj1o2o7v8eq3ce22',\n",
    "'DNT': '1',\n",
    "'Host': 'www.aaai.org',\n",
    "'Referer': 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/viewPDFInterstitial/8273/8391',\n",
    "'Sec-Fetch-Mode': 'nested-navigate',\n",
    "'Sec-Fetch-Site': 'same-origin',\n",
    "'Upgrade-Insecure-Requests': '1',\n",
    "'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.117 Safari/537.36',\n",
    "}"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "14:\n",
    "http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8273/8391\n",
    "--> https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/download/8273/8391\n",
    "\n",
    "15:\n",
    "http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9320/9217\n",
    "--> https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9320/9217\n",
    "\n",
    "16:\n",
    "http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12306/11556\n",
    "--> https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12306/11556\n",
    "\n",
    "17:\n",
    "http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14553/13735\n",
    "--> https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14553/13735\n",
    "\n",
    "18:\n",
    "https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16583/15664\n",
    "--> https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/16583/15664\n",
    "\n",
    "19:\n",
    "https://aaai.org/ojs/index.php/AAAI/article/view/3762/3640\n",
    "--> 直接下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "dw_tuple = ('AAAI_2014', 'TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation', 'https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/download/8273/8391')\n",
    "'https://aaai.org/ojs/index.php/AAAI/article/view/3762/3640'\n",
    "'https://www.aaai.org/ocs/index.php/AAAI/AAAI19/paper/download/3762/3640'\n",
    "\n",
    "paper_year = dw_tuple[0]\n",
    "org, year = paper_year.split('_')\n",
    "pdf_name = re.sub(r'[\\\\/:*?\"<>|]', \" \", dw_tuple[1].strip())\n",
    "pdf_url = dw_tuple[2]\n",
    "headers = get_headers(org, year)\n",
    "pdf_check_flag = False\n",
    "file_name = f'{paper_year}/{pdf_name}.pdf'\n",
    "if not os.path.exists(paper_year): os.mkdir(paper_year)\n",
    "\n",
    "with open(file_name,'wb') as F:\n",
    "    with requests.Session() as s:\n",
    "        F.write(requests.get(pdf_url, headers = headers2, proxies=proxies, allow_redirects=False).content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rr = requests.head(pdf_url, headers = headers, proxies=proxies, allow_redirects=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Response [302]>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rr.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pdfminer.pdfinterp import PDFResourceManager,process_pdf\n",
    "from pdfminer.converter import TextConverter\n",
    "from pdfminer.layout import LAParams\n",
    "from io import StringIO\n",
    "import re\n",
    "\n",
    "def clean_text(raw):\n",
    "    raw = raw.replace('-\\n','').replace('\\n',' ').lower().strip()\n",
    "    # full-width character --> half-width\n",
    "    def strQ2B(ustring):\n",
    "        rstring = \"\"\n",
    "        for uchar in ustring:\n",
    "            inside_code=ord(uchar)\n",
    "            if inside_code == 12288:        \n",
    "                inside_code = 32\n",
    "            elif (inside_code >= 65281 and inside_code <= 65374):\n",
    "                inside_code -= 65248\n",
    "            rstring += chr(inside_code)\n",
    "        return rstring\n",
    "    \n",
    "    # handle unseen char\n",
    "    def unseen_char(ustring):\n",
    "        return re.sub('[\\001\\002\\003\\004\\005\\006\\007\\x08\\x09\\x0a\\x0b\\x0c\\x0d\\x0e\\x0f\\x10\\x11\\x12\\x13\\x14\\x15\\x16\\x17\\x18\\x19\\x1a]+', '', ustring)\n",
    "\n",
    "    return unseen_char(strQ2B(raw))\n",
    "\n",
    "def read_pdf(file_path):\n",
    "    pdffile=open(file_path, 'rb')\n",
    "    rsrcmgr=PDFResourceManager()\n",
    "    retstr=StringIO()\n",
    "    laparams=LAParams(\n",
    "        line_overlap=0.5,\n",
    "        char_margin=2.0,\n",
    "        line_margin=0.5,\n",
    "        word_margin=0.1,\n",
    "        boxes_flow=0.5,\n",
    "        detect_vertical=False,\n",
    "        all_texts=True,\n",
    "        paragraph_indent=True,\n",
    "        heuristic_word_margin=True,\n",
    "    )\n",
    "    device=TextConverter(rsrcmgr, retstr, laparams = laparams)\n",
    "    process_pdf(rsrcmgr, device, pdffile)\n",
    "    device.close()\n",
    "    content = retstr.getvalue().replace('-\\n','').replace('\\n',' ').lower()\n",
    "    retstr.close()\n",
    "    strs = str(content)\n",
    "    return strs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = read_pdf('ACL_2014/A Bayesian Method to Incorporate Background Knowledge during Automatic Text Summarization.pdf')\n",
    "x = clean_text(x)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_words = ['content', 'approach ', 'algorithm  ', 'approach ']\n",
    "key_words = [clean_text(i) for i in sorted(set(key_words), key = key_words.index)]\n",
    "key_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_words_counts = {k:x.count(k) for k in key_words}\n",
    "key_words_counts\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pdfminer\n",
    "from pdfminer.high_level import extract_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(pdfminer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20200104'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdfminer.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['D:\\\\Anaconda3\\\\lib\\\\site-packages\\\\pdfminer']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdfminer.__path__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m\n",
       "\u001b[0mextract_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mpdf_file\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mpassword\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mpage_numbers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mmaxpages\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mcaching\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mcodec\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'utf-8'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mlaparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Parses and returns the text contained in a PDF file.\n",
       "Takes loads of optional arguments but the defaults are somewhat sane.\n",
       "Returns a string containing all of the text extracted.\n",
       "\n",
       ":param pdf_file: Path to the PDF file to be worked on\n",
       ":param password: For encrypted PDFs, the password to decrypt.\n",
       ":param page_numbers: List of zero-indexed page numbers to extract.\n",
       ":param maxpages: The maximum number of pages to parse\n",
       ":param caching: If resources should be cached\n",
       ":param codec: Text decoding codec\n",
       ":param laparams: LAParams object from pdfminer.layout.\n",
       "\u001b[1;31mFile:\u001b[0m      d:\\anaconda3\\lib\\site-packages\\pdfminer\\high_level.py\n",
       "\u001b[1;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extract_text?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "r = extract_text('ACL_2018/AMR Parsing as Graph Prediction with Latent Alignment.pdf', maxpages=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "PDFSyntaxError",
     "evalue": "No /Root object! - Is this really a PDF?",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPDFSyntaxError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-931c395fa1af>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mextract_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ACL_2019/A Turkish Dataset for Gender Identification of Twitter Users.pdf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pdfminer\\high_level.py\u001b[0m in \u001b[0;36mextract_text\u001b[1;34m(pdf_file, password, page_numbers, maxpages, caching, codec, laparams)\u001b[0m\n\u001b[0;32m    117\u001b[0m                 \u001b[0mpassword\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpassword\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m                 \u001b[0mcaching\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcaching\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m                 \u001b[0mcheck_extractable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    120\u001b[0m         ):\n\u001b[0;32m    121\u001b[0m             \u001b[0minterpreter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess_page\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pdfminer\\pdfpage.py\u001b[0m in \u001b[0;36mget_pages\u001b[1;34m(cls, fp, pagenos, maxpages, password, caching, check_extractable)\u001b[0m\n\u001b[0;32m    125\u001b[0m         \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPDFParser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    126\u001b[0m         \u001b[1;31m# Create a PDF document object that stores the document structure.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 127\u001b[1;33m         \u001b[0mdoc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPDFDocument\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparser\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpassword\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpassword\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaching\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcaching\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    128\u001b[0m         \u001b[1;31m# Check if the document allows text extraction. If not, abort.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    129\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcheck_extractable\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdoc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_extractable\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pdfminer\\pdfdocument.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, parser, password, caching, fallback)\u001b[0m\n\u001b[0;32m    582\u001b[0m                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    583\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 584\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mPDFSyntaxError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'No /Root object! - Is this really a PDF?'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    585\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcatalog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Type'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mLITERAL_CATALOG\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    586\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0msettings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSTRICT\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mPDFSyntaxError\u001b[0m: No /Root object! - Is this really a PDF?"
     ]
    }
   ],
   "source": [
    "extract_text('ACL_2019/A Turkish Dataset for Gender Identification of Twitter Users.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'ACL_2018/AMR Parsing as Graph Prediction with Latent Alignment.pdf2'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-d8b2fff61d3d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mextract_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ACL_2018/AMR Parsing as Graph Prediction with Latent Alignment.pdf2'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pdfminer\\high_level.py\u001b[0m in \u001b[0;36mextract_text\u001b[1;34m(pdf_file, password, page_numbers, maxpages, caching, codec, laparams)\u001b[0m\n\u001b[0;32m    105\u001b[0m         \u001b[0mlaparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLAParams\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 107\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpdf_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0moutput_string\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    108\u001b[0m         \u001b[0mrsrcmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPDFResourceManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    109\u001b[0m         device = TextConverter(rsrcmgr, output_string, codec=codec,\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'ACL_2018/AMR Parsing as Graph Prediction with Latent Alignment.pdf2'"
     ]
    }
   ],
   "source": [
    "extract_text('ACL_2018/AMR Parsing as Graph Prediction with Latent Alignment.pdf2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os  \n",
    "  \n",
    "def listdir(path, list_name):  \n",
    "    for file in os.listdir(path):  \n",
    "        file_path = os.path.join(path, file)  \n",
    "        if os.path.isdir(file_path):  \n",
    "            listdir(file_path, list_name)  \n",
    "        elif os.path.splitext(file_path)[1]=='.pdf':  \n",
    "            list_name.append(file_path) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = []\n",
    "listdir('./', res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8570"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__cause__', '__class__', '__context__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setstate__', '__sizeof__', '__str__', '__subclasshook__', '__suppress_context__', '__traceback__', 'args', 'characters_written', 'errno', 'filename', 'filename2', 'strerror', 'winerror', 'with_traceback']\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    open('asd.cc','r')\n",
    "except Exception as err:\n",
    "    print (dir(err))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Errno 2] No such file or directory: 'asd.cc'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    open('asd.cc','r')\n",
    "except Exception as err:\n",
    "    print (err.__str__())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "res1 = [i.split('/')[-1] for i in res]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)',\n",
       " 'Learning Ensembles of Structured Prediction Rules',\n",
       " 'Representation Learning for Text-level Discourse Parsing',\n",
       " 'Text-level Discourse Dependency Parsing',\n",
       " 'Discovering Latent Structure in Task-Oriented Dialogues',\n",
       " 'Learning Structured Perceptrons for Coreference Resolution with Latent Antecedents and Non-local Features',\n",
       " 'Multilingual Models for Compositional Distributed Semantics',\n",
       " 'Simple Negation Scope Resolution through Deep Parsing: A Semantic Solution to a Semantic Problem',\n",
       " 'Logical Inference on Dependency-based Compositional Semantics',\n",
       " 'A practical and linguistically-motivated approach to compositional distributional semantics',\n",
       " 'Lattice Desegmentation for Statistical Machine Translation',\n",
       " 'Bilingually-constrained Phrase Embeddings for Machine Translation',\n",
       " 'Learning New Semi-Supervised Deep Auto-encoder Features for Statistical Machine Translation',\n",
       " 'Learning Topic Representation for SMT with Neural Networks',\n",
       " 'Tagging The Web: Building A Robust Web Tagger with Neural Network',\n",
       " 'Unsupervised Solution Post Identification from Discussion Forums',\n",
       " 'Weakly Supervised User Profile Extraction from Twitter',\n",
       " 'The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter',\n",
       " 'Inferring User Political Preferences from Streaming Communications',\n",
       " 'Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees',\n",
       " 'Sparser, Better, Faster GPU Parsing',\n",
       " 'Shift-Reduce CCG Parsing with a Dependency Model',\n",
       " 'Less Grammar, More Features',\n",
       " 'Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors',\n",
       " 'Metaphor Detection with Cross-Lingual Model Transfer',\n",
       " 'Learning Word Sense Distributions, Detecting Unattested Senses and Identifying Novel Senses Using Topic Models',\n",
       " 'Learning to Automatically Solve Algebra Word Problems',\n",
       " 'Modelling function words improves unsupervised word segmentation',\n",
       " 'Max-Margin Tensor Neural Network for Chinese Word Segmentation',\n",
       " 'An Empirical Study on the Effect of Negation Words on Sentiment',\n",
       " 'Extracting Opinion Targets and Opinion Words from Online Reviews with Graph Co-ranking',\n",
       " 'Context-aware Learning for Sentence-level Sentiment Analysis with Posterior Regularization',\n",
       " 'Product Feature Mining: Semantic Clues versus Syntactic Constituents',\n",
       " 'Aspect Extraction with Automated Prior Knowledge Learning',\n",
       " 'Anchors Regularized: Adding Robustness and Extensibility to Scalable Topic-Modeling Algorithms',\n",
       " 'A Bayesian Mixed Effects Model of Literary Character',\n",
       " 'Collective Tweet Wikification based on Semi-supervised Graph Regularization',\n",
       " 'Zero-shot Entity Extraction from Web Pages',\n",
       " 'Incremental Joint Extraction of Entity Mentions and Relations',\n",
       " 'That’s Not What I Meant! Using Parsers to Avoid Structural Ambiguities in Generated Text',\n",
       " 'Surface Realisation from Knowledge-Bases',\n",
       " 'Hybrid Simplification using Deep Semantics and Machine Translation',\n",
       " 'Grammatical Relations in Chinese: GB-Ground Extraction and Data-Driven Parsing',\n",
       " 'Ambiguity-aware Ensemble Training for Semi-supervised Dependency Parsing',\n",
       " 'A Robust Approach to Aligning Heterogeneous Lexical Resources',\n",
       " 'Predicting the relevance of distributional semantic similarity with contextual information',\n",
       " 'Interpretable Semantic Vectors from a Joint Model of Brain- and Text- Based Meaning',\n",
       " 'Single-Agent vs. Multi-Agent Techniques for Concurrent Reinforcement Learning of Negotiation Dialogue Policies',\n",
       " 'A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing',\n",
       " 'Negation Focus Identification with Contextual Discourse Information',\n",
       " 'New Word Detection for Sentiment Analysis',\n",
       " 'ReNew: A Semi-Supervised Framework for Generating Domain-Specific Lexicons and Sentiment Analysis',\n",
       " 'A Decision-Theoretic Approach to Natural Language Generation',\n",
       " 'Generating Code-switched Text for Lexical Learning',\n",
       " 'Omni-word Feature and Soft Constraint for Chinese Relation Extraction',\n",
       " 'Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora',\n",
       " 'Learning Soft Linear Constraints with Application to Citation Field Extraction',\n",
       " 'A Study of Concept-based Weighting Regularization for Medical Records Search',\n",
       " 'Learning to Predict Distributions of Words Across Domains',\n",
       " 'How to make words with vectors: Phrase generation in distributional semantics',\n",
       " 'Vector space semantics with frequency-driven motifs',\n",
       " 'Lexical Inference over Multi-Word Predicates: A Distributional Approach',\n",
       " 'A Convolutional Neural Network for Modelling Sentences',\n",
       " 'Online Learning in Tensor Space',\n",
       " 'Graph-based Semi-Supervised Learning of Translation Models from Monolingual Data',\n",
       " 'Using Discourse Structure Improves Machine Translation Evaluation',\n",
       " 'Learning Continuous Phrase Representations for Translation Modeling',\n",
       " 'Adaptive Quality Estimation for Machine Translation',\n",
       " 'Learning Grounded Meaning Representations with Autoencoders',\n",
       " 'Joint POS Tagging and Transition-based Constituent Parsing in Chinese with Non-local Features',\n",
       " 'Strategies for Contiguous Multiword Expression Analysis and Dependency Parsing',\n",
       " 'Correcting Preposition Errors in Learner English Using Error Case Frames and Feedback Messages',\n",
       " 'Kneser-Ney Smoothing on Expected Counts',\n",
       " 'Robust Entity Clustering via Phylogenetic Inference',\n",
       " 'Linguistic Structured Sparsity in Text Categorization',\n",
       " 'Perplexity on Reduced Corpora',\n",
       " 'Robust Domain Adaptation for Relation Extraction via Clustering Consistency',\n",
       " 'Encoding Relation Requirements for Relation Extraction via Joint Inference',\n",
       " 'Medical Relation Extraction with Manifold Models',\n",
       " 'Distant Supervision for Relation Extraction with Matrix Completion',\n",
       " 'Enhancing Grammatical Cohesion: Generating Transitional Expressions for SMT',\n",
       " 'Adaptive HTER Estimation for Document-Specific MT Post-Editing',\n",
       " 'Translation Assistance by Translation of L1 Fragments in an L2 Context',\n",
       " 'Response-based Learning for Grounded Machine Translation',\n",
       " 'Modelling Events through Memory-based, Open-IE Patterns for Abstractive Summarization',\n",
       " 'Hierarchical Summarization: Scaling Up Multi-Document Summarization',\n",
       " 'Query-Chain Focused Summarization',\n",
       " 'Exploiting Timelines to Enhance Multi-document Summarization',\n",
       " 'A chance-corrected measure of inter-annotator agreement for syntax',\n",
       " 'Two Is Bigger (and Better) Than One: the Wikipedia Bitaxonomy Project',\n",
       " 'Information Extraction over Structured Data: Question Answering with Freebase',\n",
       " 'Knowledge-Based Question Answering as Machine Translation',\n",
       " 'Discourse Complements Lexical Semantics for Non-factoid Answer Reranking',\n",
       " 'Toward Future Scenario Generation: Extracting Event Causality Exploiting Semantic Relation, Context, and Association Features',\n",
       " 'Cross-narrative Temporal Ordering of Medical Events',\n",
       " 'Language-Aware Truth Assessment of Fact Candidates',\n",
       " 'That’s sick dude!: Automatic identification of word sense change across different timescales',\n",
       " 'A Step-wise Usage-based Method for Inducing Polysemy-aware Verb Classes',\n",
       " 'Structured Learning for Taxonomy Induction with Belief Propagation',\n",
       " 'A Provably Correct Learning Algorithm for Latent-Variable PCFGs',\n",
       " 'Spectral Unsupervised Parsing with Additive Tree Metrics',\n",
       " 'Weak semantic context helps phonetic learning in a model of infant language acquisition',\n",
       " 'Bootstrapping into Filler-Gap: An Acquisition Story',\n",
       " 'Nonparametric Learning of Phonological Constraints in Optimality Theory',\n",
       " 'Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy',\n",
       " 'Political Ideology Detection Using Recursive Neural Networks',\n",
       " 'A Unified Model for Soft Linguistic Reordering Constraints in Statistical Machine Translation',\n",
       " 'Are Two Heads Better than One? Crowdsourced Translation via a Two-Step Collaboration of Non-Professional Translators and Editors',\n",
       " 'A Generalized Language Model as the Combination of Skipped n-grams and Modified Kneser Ney Smoothing',\n",
       " 'A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls',\n",
       " 'Polylingual Tree-Based Topic Models for Translation Domain Adaptation',\n",
       " 'Low-Resource Semantic Role Labeling',\n",
       " 'Joint Syntactic and Semantic Parsing with Combinatory Categorial Grammar',\n",
       " 'Learning Semantic Hierarchies via Word Embeddings',\n",
       " 'Probabilistic Soft Logic for Semantic Textual Similarity',\n",
       " 'Abstractive Summarization of Spoken and Written Conversations Based on Phrasal Queries',\n",
       " 'Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data',\n",
       " 'Approximation Strategies for Multi-Structure Sentence Compression',\n",
       " 'Opinion Mining on YouTube',\n",
       " 'Automatic Keyphrase Extraction: A Survey of the State of the Art',\n",
       " 'Pattern Dictionary of English Prepositions',\n",
       " 'Looking at Unbalanced Specialized Comparable Corpora for Bilingual Lexicon Extraction',\n",
       " 'Validating and Extending Semantic Knowledge Bases using Video Games with a Purpose',\n",
       " 'Shallow Analysis Based Assessment of Syntactic Complexity for Automated Speech Scoring',\n",
       " 'Can You Repeat That? Using Word Repetition to Improve Spoken Term Detection',\n",
       " 'Character-Level Chinese Dependency Parsing',\n",
       " 'Unsupervised Dependency Parsing with Transferring Distribution via Parallel Guidance and Entropy Regularization',\n",
       " 'Unsupervised Morphology-Based Vocabulary Expansion',\n",
       " 'Toward Better Chinese Word Segmentation for SMT via Bilingual Constraints',\n",
       " 'Fast and Robust Neural Network Joint Models for Statistical Machine Translation',\n",
       " 'Low-Rank Tensors for Scoring Dependency Structures',\n",
       " 'CoSimRank: A Flexible & Efficient Graph-Theoretic Similarity Measure',\n",
       " 'Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world',\n",
       " 'Semantic Parsing via Paraphrasing',\n",
       " 'A Discriminative Graph-Based Parser for the Abstract Meaning Representation',\n",
       " 'Context-dependent Semantic Parsing for Time Expressions',\n",
       " 'Semantic Frame Identification with Distributed Word Representations',\n",
       " 'A Sense-Based Translation Model for Statistical Machine Translation',\n",
       " 'Recurrent Neural Networks for Word Alignment Model',\n",
       " 'A Constrained Viterbi Relaxation for Bidirectional Word Alignment',\n",
       " 'A Recursive Recurrent Neural Network for Statistical Machine Translation',\n",
       " 'Predicting Instructor’s Intervention in MOOC forums',\n",
       " 'A Joint Graph Model for Pinyin-to-Chinese Conversion with Typo Correction',\n",
       " 'Smart Selection',\n",
       " 'Modeling Prompt Adherence in Student Essays',\n",
       " 'ConnotationWordNet: Learning Connotation over the Word+Sense Network',\n",
       " 'Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification',\n",
       " 'Towards a General Rule for Identifying Deceptive Opinion Spam',\n",
       " 'Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)',\n",
       " 'Exploring the Relative Role of Bottom-up and Top-down Information in Phoneme Learning',\n",
       " 'Biases in Predicting the Human Language Model',\n",
       " 'Probabilistic Labeling for Efficient Referential Grounding based on Collaborative Discourse',\n",
       " 'A Composite Kernel Approach for Dialog Topic Tracking with Structured Domain Knowledge from Wikipedia',\n",
       " 'An Extension of BLANC to System Mentions',\n",
       " 'Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation',\n",
       " 'Measuring Sentiment Annotation Complexity of Text',\n",
       " 'Improving Citation Polarity Classification with Product Reviews',\n",
       " 'Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification',\n",
       " 'Sprinkling Topics for Weakly Supervised Text Classification',\n",
       " 'A Feature-Enriched Tree Kernel for Relation Extraction',\n",
       " 'Employing Word Representations and Regularization for Domain Adaptation of Relation Extraction',\n",
       " 'Graph Ranking for Collective Named Entity Disambiguation',\n",
       " 'Descending-Path Convolution Kernel for Syntactic Structures',\n",
       " 'Entities’ Sentiment Relevance',\n",
       " 'Automatic Detection of Multilingual Dictionaries on the Web',\n",
       " 'Automatic Detection of Cognates Using Orthographic Alignment',\n",
       " 'Automatically constructing Wordnet Synsets',\n",
       " 'Constructing a Turkish-English Parallel TreeBank',\n",
       " 'Improved Typesetting Models for Historical OCR',\n",
       " 'Robust Logistic Regression using Shift Parameters',\n",
       " 'Faster Phrase-Based Decoding by Refining Feature State',\n",
       " 'Decoder Integration and Expected BLEU Training for Recurrent Neural Network Language Models',\n",
       " 'On the Elements of an Accurate Tree-to-String Machine Translation System',\n",
       " 'Simple extensions and POS Tags for a reparameterised IBM Model 2',\n",
       " 'Dependency-based Pre-ordering for Chinese-English Machine Translation',\n",
       " 'Generalized Character-Level Spelling Error Correction',\n",
       " 'Improved Iterative Correction for Distant Spelling Errors',\n",
       " 'Predicting Grammaticality on an Ordinal Scale',\n",
       " 'I’m a Belieber: Social Roles via Self-identification and Conceptual Attributes',\n",
       " 'Automatically Detecting Corresponding Edit-Turn-Pairs in Wikipedia',\n",
       " 'Two Knives Cut Better Than One: Chinese Word Segmentation with Dual Decomposition',\n",
       " 'Effective Document-Level Features for Chinese Patent Word Segmentation',\n",
       " 'Word Segmentation of Informal Arabic with Domain Adaptation',\n",
       " 'Resolving Lexical Ambiguity in Tensor Regression Models of Meaning',\n",
       " 'A Novel Content Enriching Model for Microblog Using News Corpus',\n",
       " 'Learning Bilingual Word Representations by Marginalizing Alignments',\n",
       " 'Detecting Retries of Voice Search Queries',\n",
       " 'Sliding Alignment Windows for Real-Time Crowd Captioning',\n",
       " 'Detection of Topic and its Extrinsic Evaluation Through Multi-Document Summarization',\n",
       " 'Content Importance Models for Scoring Writing From Sources',\n",
       " 'Chinese Morphological Analysis with Character-level POS Tagging',\n",
       " 'Part-of-Speech Tagging using Conditional Random Fields: Exploiting Sub-Label Dependencies for Improved Accuracy',\n",
       " 'POS induction with distributional and morphological information using a distance-dependent Chinese restaurant process',\n",
       " 'Improving the Recognizability of Syntactic Relations Using Contextualized Examples',\n",
       " 'How to Speak a Language without Knowing It',\n",
       " 'Assessing the Discourse Factors that Influence the Quality of Machine Translation',\n",
       " 'Automatic Detection of Machine Translated Text and Translation Quality Estimation',\n",
       " 'Improving sparse word similarity models with asymmetric measures',\n",
       " 'Dependency-Based Word Embeddings',\n",
       " 'Vector spaces for historical linguistics: Using distributional semantics to study syntactic productivity in diachrony',\n",
       " 'Single Document Summarization based on Nested Tree Structure',\n",
       " 'Linguistic Considerations in Automatic Question Generation',\n",
       " 'Polynomial Time Joint Structural Inference for Sentence Compression',\n",
       " 'A Bayesian Method to Incorporate Background Knowledge during Automatic Text Summarization',\n",
       " 'Predicting Power Relations between Participants in Written Dialog from a Single Thread',\n",
       " 'Tri-Training for Authorship Attribution with Limited Training Data',\n",
       " 'Automation and Evaluation of the Keyword Method for Second Language Learning',\n",
       " 'Citation Resolution: A method for evaluating context-based citation recommendation systems',\n",
       " 'Hippocratic Abbreviation Expansion',\n",
       " 'Unsupervised Feature Learning for Visual Sign Language Identification',\n",
       " 'Experiments with crowdsourced re-annotation of a POS tagging data set',\n",
       " 'Building Sentiment Lexicons for All Major Languages',\n",
       " 'Difficult Cases: From Data to Learning, and Back',\n",
       " 'The VerbCorner Project: Findings from Phase 1 of crowd-sourcing a semantic decomposition of verbs',\n",
       " 'A Corpus of Sentence-level Revisions in Academic Writing: A Step towards Understanding Statement Strength in Communication',\n",
       " 'Determiner-Established Deixis to Communicative Artifacts in Pedagogical Text',\n",
       " 'Modeling Factuality Judgments in Social Media Text',\n",
       " 'A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon',\n",
       " 'Depeche Mood: a Lexicon for Emotion Analysis from Crowd Annotated News',\n",
       " 'Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training',\n",
       " 'Cross-cultural Deception Detection',\n",
       " 'Particle Filter Rejuvenation and Latent Dirichlet Allocation',\n",
       " 'Comparing Automatic Evaluation Measures for Image Description',\n",
       " 'Learning a Lexical Simplifier Using Wikipedia',\n",
       " 'Cheap and easy entity evaluation',\n",
       " 'Identifying Real-Life Complex Task Names with Task-Intrinsic Entities from Microblogs',\n",
       " 'Mutual Disambiguation for Entity Linking',\n",
       " 'How Well can We Learn Interpretable Entity Types from Text?',\n",
       " 'Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval',\n",
       " 'Two-Stage Hashing for Fast Document Retrieval',\n",
       " 'An Annotation Framework for Dense Event Ordering',\n",
       " 'Linguistically debatable or just plain wrong?',\n",
       " 'Humans Require Context to Infer Ironic Intent (so Computers Probably do, too)',\n",
       " 'Automatic prediction of aspectual class of verbs in context',\n",
       " 'Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification',\n",
       " 'Applying a Naive Bayes Similarity Measure to Word Sense Disambiguation',\n",
       " 'Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout',\n",
       " 'Improving Lexical Embeddings with Semantic Knowledge',\n",
       " 'Optimizing Segmentation Strategies for Simultaneous Speech Translation',\n",
       " 'A joint inference of deep case analysis and zero subject generation for Japanese-to-English statistical machine translation',\n",
       " 'A Hybrid Approach to Skeleton-based Translation',\n",
       " 'Effective Selection of Translation Model Training Data',\n",
       " 'Refinements to Interactive Translation Prediction Based on Search Graphs',\n",
       " 'Cross-lingual Model Transfer Using Feature Representation Projection',\n",
       " 'Cross-language and Cross-encyclopedia Article Linking Using Mixed-language Topic Model and Hypernym Translation',\n",
       " 'Nonparametric Method for Data-driven Image Captioning',\n",
       " 'Improved Correction Detection in Revised ESL Sentences',\n",
       " 'Unsupervised Alignment of Privacy Policies using Hidden Markov Models',\n",
       " 'Enriching Cold Start Personalized Language Model Using Social Network Information',\n",
       " 'Automatic Labelling of Topic Models Learned from Twitter by Summarisation',\n",
       " 'Stochastic Contextual Edit Distance and Probabilistic FSTs',\n",
       " 'Labelling Topics using Unsupervised Graph-based Methods',\n",
       " 'Training a Korean SRL System with Rich Morphological Features',\n",
       " 'Semantic Parsing for Single-Relation Question Answering',\n",
       " 'On WordNet Semantic Classes and Dependency Parsing',\n",
       " 'Enforcing Structural Diversity in Cube-pruned Dependency Parsing',\n",
       " 'The Penn Parsed Corpus of Modern British English: First Parsing Results and Analysis',\n",
       " 'Parser Evaluation Using Derivation Trees: A Complement to evalb',\n",
       " 'Learning Polylingual Topic Models from Code-Switched Social Media Documents',\n",
       " 'Normalizing tweets with edit scripts and recurrent neural embeddings',\n",
       " 'Exponential Reservoir Sampling for Streaming Language Models',\n",
       " 'A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection',\n",
       " 'A Simple Bayesian Modelling Approach to Event Extraction from Twitter',\n",
       " 'Be Appropriate and Funny: Automatic Entity Morph Encoding',\n",
       " 'Applying Grammar Induction to Text Mining',\n",
       " 'Semantic Consistency: A Local Subspace Based Method for Distant Supervised Relation Extraction',\n",
       " 'Concreteness and Subjectivity as Dimensions of Lexical Meaning',\n",
       " 'Infusion of Labeled Data into Distant Supervision for Relation Extraction',\n",
       " 'Recognizing Implied Predicate-Argument Relationships in Textual Inference',\n",
       " 'Measuring metaphoricity',\n",
       " 'Empirical Study of Unsupervised Chinese Word Segmentation Methods for SMT on Large-scale Corpora',\n",
       " 'EM Decipherment for Large Vocabularies',\n",
       " 'XMEANT: Better semantic MT evaluation without reference translations',\n",
       " 'Sentence Level Dialect Identification for Machine Translation System Selection',\n",
       " 'RNN-based Derivation Structure Prediction for SMT',\n",
       " 'Hierarchical MT Training using Max-Violation Perceptron',\n",
       " 'Punctuation Processing for Projective Dependency Parsing',\n",
       " 'Transforming trees into hedges and parsing with “hedgebank” grammars',\n",
       " 'Incremental Predictive Parsing with TurboParser',\n",
       " 'Tailoring Continuous Word Representations for Dependency Parsing',\n",
       " 'Observational Initialization of Type-Supervised Taggers',\n",
       " 'How much do word embeddings encode about syntax?',\n",
       " 'Distributed Representations of Geographically Situated Language',\n",
       " 'Improving Multi-Modal Representations Using Image Dispersion: Why Less is Sometimes More',\n",
       " 'Bilingual Event Extraction: a Case Study on Trigger Type Determination',\n",
       " 'Understanding Relation Temporality of Entities',\n",
       " 'Does the Phonology of L1 Show Up in L2 Texts?',\n",
       " 'Cross-lingual Opinion Analysis via Negative Transfer Detection',\n",
       " 'Proceedings of the ACL 2014 Student Research Workshop',\n",
       " 'Bayesian Kernel Methods for Natural Language Processing',\n",
       " 'Extracting Temporal and Causal Relations between Events',\n",
       " 'Towards a discourse relation-aware approach for Chinese-English machine translation',\n",
       " 'Analyzing Positions and Topics in Political Discussions of the German Bundestag',\n",
       " 'A Mapping-Based Approach for General Formal Human Computer Interaction Using Natural Language',\n",
       " 'An Exploration of Embeddings for Generalized Phrases',\n",
       " 'Learning Grammar with Explicit Annotations for Subordinating Conjunctions',\n",
       " 'Going beyond sentences when applying tree kernels',\n",
       " 'Multi-document summarization using distortion-rate ratio',\n",
       " 'Disambiguating prepositional phrase attachment sites with sense information captured in contextualized distributional data',\n",
       " 'Open Information Extraction for Spanish Language based on Syntactic Constraints',\n",
       " 'Improving Text Normalization via Unsupervised Model and Discriminative Reranking',\n",
       " 'Semi-Automatic Development of KurdNet, The Kurdish WordNet',\n",
       " 'Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations',\n",
       " 'Cross-Lingual Information to the Rescue in Keyword Extraction',\n",
       " 'Visualization, Search, and Error Analysis for Coreference Annotations',\n",
       " 'Open-Source Tools for Morphology, Lemmatization, POS Tagging and Named Entity Recognition',\n",
       " 'Community Evaluation and Exchange of Word Vectors at wordvectors.org',\n",
       " 'WINGS:Writing with Intelligent Guidance and Suggestions',\n",
       " 'DKPro Keyphrases: Flexible and Reusable Keyphrase Extraction Experiments',\n",
       " 'Real-Time Detection, Tracking, and Monitoring of Automatically Discovered Events in Social Media',\n",
       " 'The Excitement Open Platform for Textual Inferences',\n",
       " 'WELT: Using Graphics Generation in Linguistic Fieldwork',\n",
       " 'The Stanford CoreNLP Natural Language Processing Toolkit',\n",
       " 'DKPro TC: A Java-based Framework for Supervised Learning Experiments on Textual Data',\n",
       " 'WoSIT: A Word Sense Induction Toolkit for Search Result Clustering and Diversification',\n",
       " 'A Rule-Augmented Statistical Phrase-based Translation System',\n",
       " 'KyotoEBMT: An Example-Based Dependency-to-Dependency Translation Framework',\n",
       " 'kLogNLP: Graph Kernel–based Relational Learning of Natural Language',\n",
       " 'Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno',\n",
       " 'Web Information Mining and Decision Support Platform for the Modern Service Industry',\n",
       " 'FAdR: A System for Recognizing False Online Advertisements',\n",
       " 'lex4all: A language-independent tool for building and evaluating pronunciation lexicons for small-vocabulary speech recognition',\n",
       " 'Enhanced Search with Wildcards and Morphological Inflections in the Google Books Ngram Viewer',\n",
       " 'Simplified Dependency Annotations with GFL-Web',\n",
       " 'Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: Tutorials',\n",
       " 'Gaussian Processes for Natural Language Processing',\n",
       " 'Scalable Large-Margin Structured Learning: Theory and Algorithms',\n",
       " 'Semantics for Large-Scale Multimedia: New Challenges for NLP',\n",
       " 'Wikification and Beyond: The Challenges of Entity and Concept Grounding',\n",
       " 'New Directions in Vector Space Models of Meaning',\n",
       " 'Structured Belief Propagation for NLP',\n",
       " 'Semantics, Discourse and Statistical Machine Translation',\n",
       " 'Syntactic Processing Using Global Discriminative Learning and Beam-Search Decoding',\n",
       " 'Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)',\n",
       " 'On Using Very Large Target Vocabulary for Neural Machine Translation',\n",
       " 'Addressing the Rare Word Problem in Neural Machine Translation',\n",
       " 'Encoding Source Language with Convolutional Neural Network for Machine Translation',\n",
       " 'Statistical Machine Translation Features with Multitask Tensor Networks',\n",
       " 'Describing Images using Inferred Visual Dependency Representations',\n",
       " 'Text to 3D Scene Generation with Rich Lexical Grounding',\n",
       " 'MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity',\n",
       " 'Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums',\n",
       " 'Semantically Smooth Knowledge Graph Embedding',\n",
       " 'SensEmbed: Learning Sense Embeddings for Word and Relational Similarity',\n",
       " 'Revisiting Word Embedding for Contrasting Meaning',\n",
       " 'Joint Models of Disagreement and Stance in Online Debate',\n",
       " 'Low-Rank Regularization for Sparse Conjunctive Feature Spaces: An Application to Named Entity Classification',\n",
       " 'Learning Word Representations by Jointly Modeling Syntagmatic and Paradigmatic Relations',\n",
       " 'Learning Dynamic Feature Selection for Fast Sequential Prediction',\n",
       " 'Compositional Vector Space Models for Knowledge Base Completion',\n",
       " 'Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks',\n",
       " 'Stacked Ensembles of Information Extractors for Knowledge-Base Population',\n",
       " 'Generative Event Schema Induction with Entity Disambiguation',\n",
       " 'Syntax-based Simultaneous Translation through Prediction of Unseen Syntactic Constituents',\n",
       " 'Efficient Top-Down BTG Parsing for Machine Translation Preordering',\n",
       " 'Online Multitask Learning for Machine Translation Quality Estimation',\n",
       " 'A Context-Aware Topic Model for Statistical Machine Translation',\n",
       " 'Learning Answer-Entailing Structures for Machine Comprehension',\n",
       " 'Learning Continuous Word Embedding with Metadata for Question Retrieval in Community Question Answering',\n",
       " 'Question Answering over Freebase with Multi-Column Convolutional Neural Networks',\n",
       " 'Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot Learning',\n",
       " 'A Generalisation of Lexical Functions for Composition in Distributional Semantics',\n",
       " 'Simple Learning and Compositional Application of Perceptually Grounded Word Meanings for Incremental Reference Resolution',\n",
       " 'Neural CRF Parsing',\n",
       " 'An Effective Neural Network Model for Graph-based Dependency Parsing',\n",
       " 'Structured Training for Neural Network Transition-Based Parsing',\n",
       " 'Transition-Based Dependency Parsing with Stack Long Short-Term Memory',\n",
       " 'Leveraging Linguistic Structure For Open Domain Information Extraction',\n",
       " 'Joint Information Extraction and Reasoning: A Scalable Statistical Relational Learning Approach',\n",
       " 'A Knowledge-Intensive Model for Prepositional Phrase Attachment',\n",
       " 'A Convolution Kernel Approach to Identifying Comparisons in Text',\n",
       " 'It Depends: Dependency Parser Comparison Using A Web-based Evaluation Tool',\n",
       " 'Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling',\n",
       " 'Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies',\n",
       " 'Learning to Adapt Credible Knowledge in Cross-lingual Sentiment Analysis',\n",
       " 'Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification',\n",
       " 'Content Models for Survey Generation: A Factoid-Based Evaluation',\n",
       " 'Training a Natural Language Generator From Unaligned Data',\n",
       " 'Event-Driven Headline Generation',\n",
       " 'New Transfer Learning Techniques for Disparate Label Sets',\n",
       " 'Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding',\n",
       " 'Efficient Disfluency Detection with Transition-based Parsing',\n",
       " 'S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking',\n",
       " 'Entity Retrieval via Entity Factoid Hierarchy',\n",
       " 'Encoding Distributional Semantics into Triple-Based Knowledge Ranking for Document Enrichment',\n",
       " 'A Strategic Reasoning Model for Generating Alternative Answers',\n",
       " 'Modeling Argument Strength in Student Essays',\n",
       " 'Summarization of Multi-Document Topic Hierarchies using Submodular Mixtures',\n",
       " 'Learning to Explain Entity Relationships in Knowledge Graphs',\n",
       " 'Bring you to the past: Automatic Generation of Topically Relevant Event Chronicles',\n",
       " 'Context-aware Entity Morph Decoding',\n",
       " 'Multi-Objective Optimization for the Joint Disambiguation of Nouns and Named Entities',\n",
       " 'Building a Scientific Concept Hierarchy Database (SCHBase)',\n",
       " 'Sentiment-Aspect Extraction based on Restricted Boltzmann Machines',\n",
       " 'Classifying Relations by Ranking with Convolutional Neural Networks',\n",
       " 'Semantic Representations for Domain Adaptation: A Case Study on the Tree Kernel-based Method for Relation Extraction',\n",
       " 'Omnia Mutantur, Nihil Interit: Connecting Past with Present by Finding Corresponding Terms across Time',\n",
       " 'Negation and Speculation Identification in Chinese Language',\n",
       " 'Learning Relational Features with Backward Random Walks',\n",
       " 'Learning the Semantics of Manipulation Action',\n",
       " 'Knowledge Graph Embedding via Dynamic Mapping Matrix',\n",
       " 'How Far are We from Fully Automatic High Quality Grammatical Error Correction?',\n",
       " 'Knowledge Portability with Semantic Expansion of Ontology Labels',\n",
       " 'Automatic disambiguation of English puns',\n",
       " 'Unsupervised Cross-Domain Word Representation Learning',\n",
       " 'A Unified Multilingual Semantic Representation of Concepts',\n",
       " 'Demographic Factors Improve Classification Performance',\n",
       " 'Vector-space calculation of semantic surprisal for predicting word pronunciation duration',\n",
       " 'Efficient Methods for Inferring Large Sparse Topic Hierarchies',\n",
       " 'Trans-dimensional Random Fields for Language Modeling',\n",
       " 'Gaussian LDA for Topic Models with Word Embeddings',\n",
       " 'Pairwise Neural Machine Translation Evaluation',\n",
       " 'String-to-Tree Multi Bottom-up Tree Transducers',\n",
       " 'Non-linear Learning for Statistical Machine Translation',\n",
       " 'Unifying Bayesian Inference and Vector Space Models for Improved Decipherment',\n",
       " 'Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation',\n",
       " 'Detecting Deceptive Groups Using Conversations and Network Analysis',\n",
       " 'WikiKreator: Improving Wikipedia Stubs Automatically',\n",
       " 'Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes',\n",
       " 'Deep Questions without Deep Understanding',\n",
       " 'The NL2KR Platform for building Natural Language Translation Systems',\n",
       " 'Multiple Many-to-Many Sequence Alignment for Combining String-Valued Variables: A G2P Experiment',\n",
       " 'Tweet Normalization with Syllables',\n",
       " 'Improving Named Entity Recognition in Tweets via Detecting Non-Standard Words',\n",
       " 'A Unified Kernel Approach for Learning Typed Sentence Rewritings',\n",
       " 'Perceptually Grounded Selectional Preferences',\n",
       " 'Joint Case Argument Identification for Japanese Predicate Argument Structure Analysis',\n",
       " 'Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model',\n",
       " 'Robust Subgraph Generation Improves Abstract Meaning Representation Parsing',\n",
       " 'Environment-Driven Lexicon Induction for High-Level Instructions',\n",
       " 'Structural Representations for Learning Relations between Pairs of Texts',\n",
       " 'Learning Semantic Representations of Users and Products for Document Level Sentiment Classification',\n",
       " 'Towards Debugging Sentiment Lexicons',\n",
       " 'Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment',\n",
       " 'Sentence-level Emotion Classification with Label and Context Dependence',\n",
       " 'Co-training for Semi-supervised Sentiment Classification Based on Dual-view Bags-of-words Representation',\n",
       " 'Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user’s likes and dislikes',\n",
       " 'Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces',\n",
       " 'Automatic Spontaneous Speech Grading: A Novel Feature Derivation Technique using the Crowd',\n",
       " 'Driving ROVER with Segment-based ASR Quality Estimation',\n",
       " 'A Hierarchical Neural Autoencoder for Paragraphs and Documents',\n",
       " 'Joint Dependency Parsing and Multiword Expression Tokenization',\n",
       " 'End-to-end learning of semantic role labeling using recurrent neural networks',\n",
       " 'Feature Optimization for Constituent Parsing via Neural Networks',\n",
       " 'Identifying Cascading Errors using Constraints in Dependency Parsing',\n",
       " 'A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network',\n",
       " 'Transition-based Neural Constituent Parsing',\n",
       " 'Feature Selection in Kernel Space: A Case Study on Dependency Parsing',\n",
       " 'Semantic Role Labeling Improves Incremental Parsing',\n",
       " 'Discontinuous Incremental Shift-reduce Parsing',\n",
       " 'A Neural Probabilistic Structured-Prediction Model for Transition-Based Dependency Parsing',\n",
       " 'Parsing Paraphrases with Joint Inference',\n",
       " 'Cross-lingual Dependency Parsing Based on Distributed Representations',\n",
       " 'Can Natural Language Processing Become Natural Language Coaching?',\n",
       " 'Machine Comprehension with Discourse Relations',\n",
       " 'Implicit Role Linking on Chinese Discourse: Exploiting Explicit Roles and Frame-to-Frame Relations',\n",
       " 'Discourse-sensitive Automatic Identification of Generic Expressions',\n",
       " 'Model-based Word Embeddings from Decompositions of Count Matrices',\n",
       " 'Entity Hierarchy Embedding',\n",
       " 'Orthogonality of Syntax and Semantics within Distributional Spaces',\n",
       " 'Scalable Semantic Parsing with Partial Ontologies',\n",
       " 'Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base',\n",
       " 'Building a Semantic Parser Overnight',\n",
       " 'Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory',\n",
       " 'Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction',\n",
       " 'Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network',\n",
       " 'A convex and feature-rich discriminative approach to dependency grammar induction',\n",
       " 'Parse Imputation for Dependency Annotations',\n",
       " 'Probing the Linguistic Strengths and Limitations of Unsupervised Grammar Induction',\n",
       " 'Entity-Centric Coreference Resolution with Model Stacking',\n",
       " 'Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution',\n",
       " 'Transferring Coreference Resolvers with Posterior Regularization',\n",
       " 'Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress',\n",
       " 'KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts',\n",
       " 'A Computationally Efficient Algorithm for Learning Topical Collocation Models',\n",
       " 'Compositional Semantic Parsing on Semi-Structured Tables',\n",
       " 'Graph parsing with s-graph grammars',\n",
       " 'Sparse Overcomplete Word Vector Representations',\n",
       " 'Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints',\n",
       " 'Adding Semantics to Data-Driven Paraphrasing',\n",
       " 'Parsing as Reduction',\n",
       " 'Optimal Shift-Reduce Constituent Parsing with Structured Perceptron',\n",
       " 'A Data-Driven, Factorization Parser for CCG Dependency Structures',\n",
       " 'Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks',\n",
       " 'genCNN: A Convolutional Architecture for Word Sequence Prediction',\n",
       " 'Neural Responding Machine for Short-Text Conversation',\n",
       " 'Abstractive Multi-Document Summarization via Phrase Selection and Merging',\n",
       " 'Joint Graphical Models for Date Selection in Timeline Summarization',\n",
       " 'Predicting Salient Updates for Disaster Summarization',\n",
       " 'Unsupervised Prediction of Acceptability Judgements',\n",
       " 'A Frame of Mind: Using Statistical Models for Detection of Framing and Agenda Setting Campaigns',\n",
       " 'Why discourse affects speakers’ choice of referring expressions',\n",
       " 'Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game',\n",
       " 'Who caught a cold ? - Identifying the subject of a symptom',\n",
       " 'Weakly Supervised Role Identification in Teamwork Interactions',\n",
       " 'Deep Unordered Composition Rivals Syntactic Methods for Text Classification',\n",
       " 'SOLAR: Scalable Online Learning Algorithms for Ranking',\n",
       " 'Text Categorization as a Graph Classification Problem',\n",
       " 'Inverted indexing for cross-lingual NLP',\n",
       " 'Multi-Task Learning for Multiple Language Translation',\n",
       " 'Accurate Linear-Time Chinese Word Segmentation via Embedding Matching',\n",
       " 'Gated Recursive Neural Network for Chinese Word Segmentation',\n",
       " 'An analysis of the user occupational class through Twitter content',\n",
       " 'Tracking unbounded Topic Streams',\n",
       " 'Inducing Word and Part-of-Speech with Pitman-Yor Hidden Semi-Markov Models',\n",
       " 'Coupled Sequence Labeling on Heterogeneous Annotations: POS Tagging as a Case Study',\n",
       " 'AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes',\n",
       " 'Improving Evaluation of Machine Translation Quality Estimation',\n",
       " 'Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)',\n",
       " 'A Framework for the Construction of Monolingual and Cross-lingual Word Similarity Datasets',\n",
       " 'On metric embedding for boosting semantic similarity computations',\n",
       " 'Improving Distributed Representation of Word Sense via WordNet Gloss Composition and Context Clustering',\n",
       " 'A Multitask Objective to Inject Lexical Contrast into Distributional Semantics',\n",
       " 'Semi-Stacking for Semi-supervised Sentiment Classification',\n",
       " 'Deep Markov Neural Network for Sequential Data Classification',\n",
       " 'Semantic Analysis and Helpfulness Prediction of Text for Online Product Reviews',\n",
       " 'Document Classification by Inversion of Distributed Language Representations',\n",
       " 'Using Tweets to Help Sentence Compression for News Highlights Generation',\n",
       " 'Domain-Specific Paraphrase Extraction',\n",
       " 'Simplifying Lexical Simplification: Do We Need Simplified Corpora?',\n",
       " 'Zoom: a corpus of natural language descriptions of map locations',\n",
       " 'Generating overspecified referring expressions: the role of discrimination',\n",
       " 'Using prosodic annotations to improve coreference resolution of spoken text',\n",
       " 'Spectral Semi-Supervised Discourse Relation Classification',\n",
       " 'I do not disagree: leveraging monolingual alignment to detect disagreement in dialogue',\n",
       " 'Language Models for Image Captioning: The Quirks and What Works',\n",
       " 'A Distributed Representation Based Query Expansion Approach for Image Captioning',\n",
       " 'Learning language through pictures',\n",
       " 'Exploiting Image Generality for Lexical Entailment Detection',\n",
       " 'Lexicon Stratification for Translating Out-of-Vocabulary Words',\n",
       " 'Recurrent Neural Network based Rule Sequence Model for Statistical Machine Translation',\n",
       " 'Discriminative Preordering Meets Kendall’s 𝜏 Maximization',\n",
       " 'Evaluating Machine Translation Systems with Second Language Proficiency Tests',\n",
       " 'Representation Based Translation Evaluation Metrics',\n",
       " 'Exploring the Planet of the APEs: a Comparative Study of State-of-the-art Methods for MT Automatic Post-Editing',\n",
       " 'Efficient Learning for Undirected Topic Models',\n",
       " 'A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features',\n",
       " 'Dependency-based Convolutional Neural Networks for Sentence Embedding',\n",
       " 'Non-Linear Text Regression with a Deep Convolutional Neural Network',\n",
       " 'A Unified Learning Framework of Skip-Grams and Global Vectors',\n",
       " 'Pre-training of Hidden-Unit CRFs',\n",
       " 'Distributional Neural Networks for Automatic Resolution of Crossword Puzzles',\n",
       " 'Word Order Typology through Multilingual Word Alignment',\n",
       " 'Measuring idiosyncratic interests in children with autism',\n",
       " 'Frame-Semantic Role Labeling with Heterogeneous Annotations',\n",
       " 'Semantic Interpretation of Superlative Expressions via Structured Knowledge Bases',\n",
       " 'Grounding Semantics in Olfactory Perception',\n",
       " 'Word-based Japanese typed dependency parsing with grammatical function analysis',\n",
       " 'KLcpos3 - a Language Similarity Measure for Delexicalized Parser Transfer',\n",
       " 'CCG Supertagging with a Recurrent Neural Network',\n",
       " 'An Efficient Dynamic Oracle for Unrestricted Non-Projective Parsing',\n",
       " 'Synthetic Word Parsing Improves Chinese Word Segmentation',\n",
       " 'If all you have is a bit of the Bible: Learning POS taggers for truly low-resource languages',\n",
       " 'Improving distant supervision using inference learning',\n",
       " 'A Lexicalized Tree Kernel for Open Information Extraction',\n",
       " 'A Dependency-Based Neural Network for Relation Classification',\n",
       " 'Embedding Methods for Fine Grained Entity Type Classification',\n",
       " 'Sieve-Based Entity Linking for the Biomedical Domain',\n",
       " 'Open IE as an Intermediate Structure for Semantic Tasks',\n",
       " 'Recovering dropped pronouns from Chinese text messages',\n",
       " 'The Users Who Say ‘Ni’: Audience Identification in Chinese-language Restaurant Reviews',\n",
       " 'Chinese Zero Pronoun Resolution: A Joint Unsupervised Discourse-Aware Model Rivaling State-of-the-Art Resolvers',\n",
       " 'Co-Simmate: Quick Retrieving All Pairwise Co-Simrank Scores',\n",
       " 'Retrieval of Research-level Mathematical Information Needs: A Test Collection and Technical Terminology Experiment',\n",
       " 'Learning to Mine Query Subtopics from Query Log',\n",
       " 'Learning Topic Hierarchies for Wikipedia Categories',\n",
       " 'Semantic Clustering and Convolutional Neural Network for Short Text Categorization',\n",
       " 'Document Level Time-anchoring for TimeLine Extraction',\n",
       " 'Event Detection and Domain Adaptation with Convolutional Neural Networks',\n",
       " 'Seed-Based Event Trigger Labeling: How far can event descriptions get us?',\n",
       " 'An Empirical Study of Chinese Name Matching and Applications',\n",
       " 'Language Identification and Modeling in Specialized Hardware',\n",
       " 'Cross-lingual Transfer of Named Entity Recognizers without Parallel Corpora',\n",
       " 'Robust Multi-Relational Clustering via ℓ1-Norm Symmetric Nonnegative Matrix Factorization',\n",
       " 'Painless Labeling with Application to Text Mining',\n",
       " 'FrameNet+: Fast Paraphrastic Tripling of FrameNet',\n",
       " 'IWNLP: Inverse Wiktionary for Natural Language Processing',\n",
       " 'TR9856: A Multi-word Term Relatedness Benchmark',\n",
       " 'PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification',\n",
       " 'Automatic Discrimination between Cognates and Borrowings',\n",
       " 'The Media Frames Corpus: Annotations of Frames Across Issues',\n",
       " 'deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets',\n",
       " 'Tibetan Unknown Word Identification from News Corpora for Supporting Lexicon-based Tibetan Word Segmentation',\n",
       " 'Learning Lexical Embeddings with Syntactic and Lexicographic Knowledge',\n",
       " 'Non-distributional Word Vector Representations',\n",
       " 'Early and Late Combinations of Criteria for Reranking Distributional Thesauri',\n",
       " 'Dependency length minimisation effects in short spans: a large-scale analysis of adjective placement in complex noun phrases',\n",
       " 'Tagging Performance Correlates with Author Age',\n",
       " 'User Based Aggregation for Biterm Topic Model',\n",
       " 'The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models',\n",
       " 'Unsupervised Decomposition of a Multi-Author Document Based on Naive-Bayesian Model',\n",
       " 'Extended Topic Model for Word Dependency',\n",
       " 'Dependency Recurrent Neural Language Models for Sentence Completion',\n",
       " 'Point Process Modelling of Rumour Dynamics in Social Media',\n",
       " 'Learning Hidden Markov Models with Distributed State Representations for Domain Adaptation',\n",
       " 'MT Quality Estimation for Computer-assisted Translation: Does it Really Help?',\n",
       " 'Context-Dependent Translation Selection Using Convolutional Neural Network',\n",
       " 'Learning Word Reorderings for Hierarchical Phrase-based Statistical Machine Translation',\n",
       " 'UNRAVEL—A Decipherment Toolkit',\n",
       " 'Multi-Pass Decoding With Complex Feature Guidance for Statistical Machine Translation',\n",
       " 'What’s in a Domain? Analyzing Genre and Topic Differences in Statistical Machine Translation',\n",
       " 'Learning Cross-lingual Word Embeddings via Matrix Co-factorization',\n",
       " 'Improving Pivot Translation by Remembering the Pivot',\n",
       " 'BrailleSUM: A News Summarization System for the Blind and Visually Impaired People',\n",
       " 'Automatic Identification of Age-Appropriate Ratings of Song Lyrics',\n",
       " 'Ground Truth for Grammatical Error Correction Metrics',\n",
       " 'Radical Embedding: Delving Deeper to Chinese Radicals',\n",
       " 'Automatic Detection of Sentence Fragments',\n",
       " 'A Computational Approach to Automatic Prediction of Drunk-Texting',\n",
       " 'Reducing infrequent-token perplexity via variational corpora',\n",
       " 'A Hierarchical Knowledge Representation for Expert Finding on Social Media',\n",
       " 'Tackling Sparsity, the Achilles Heel of Social Networks: Language Model Smoothing via Social Regularization',\n",
       " 'Twitter User Geolocation Using a Unified Text and Network Prediction Model',\n",
       " 'Automatic Keyword Extraction on Twitter',\n",
       " 'Towards a Contextual Pragmatic Model to Detect Irony in Tweets',\n",
       " 'Annotation and Classification of an Email Importance Corpus',\n",
       " 'Lexical Comparison Between Wikipedia and Twitter Corpora by Using Word Embeddings',\n",
       " 'The Discovery of Natural Typing Annotations: User-produced Potential Chinese Word Delimiters',\n",
       " 'One Tense per Scene: Predicting Tense in Chinese Conversations',\n",
       " 'A Language-Independent Feature Schema for Inflectional Morphology',\n",
       " 'Rhetoric Map of an Answer to Compound Queries',\n",
       " 'Thread-Level Information for Comment Classification in Community Question Answering',\n",
       " 'Learning Hybrid Representations to Retrieve Semantically Equivalent Questions',\n",
       " 'Machine Comprehension with Syntax, Frames, and Semantics',\n",
       " 'A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering',\n",
       " 'Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering',\n",
       " 'Bilingual Word Embeddings from Non-Parallel Document-Aligned Data Applied to Bilingual Lexicon Induction',\n",
       " 'How Well Do Distributional Models Capture Different Types of Semantic Knowledge?',\n",
       " 'Low-Rank Tensors for Verbs in Compositional Distributional Semantics',\n",
       " 'Constrained Semantic Forests for Improved Discriminative Semantic Parsing',\n",
       " 'Automatic Identification of Rhetorical Questions',\n",
       " 'Lifelong Learning for Sentiment Classification',\n",
       " 'Harnessing Context Incongruity for Sarcasm Detection',\n",
       " 'Emotion Detection in Code-switching Texts via Bilingual and Sentimental Information',\n",
       " 'Model Adaptation for Personalized Opinion Analysis',\n",
       " 'Linguistic Template Extraction for Recognizing Reader-Emotion and Emotional Resonance Writing Assistance',\n",
       " 'Aspect-Level Cross-lingual Sentiment Classification with Constrained SMT',\n",
       " 'Predicting Valence-Arousal Ratings of Words Using a Weighted Graph Method',\n",
       " 'Multi-domain Dialog State Tracking using Recurrent Neural Networks',\n",
       " 'Dialogue Management based on Sentence Clustering',\n",
       " 'Compact Lexicon Selection with Spectral Methods',\n",
       " 'The Impact of Listener Gaze on Predicting Reference Resolution',\n",
       " 'A Simultaneous Recognition Framework for the Spoken Language Understanding Module of Intelligent Personal Assistant Software on Smart Phones',\n",
       " 'A Deeper Exploration of the Standard PB-SMT Approach to Text Simplification and its Evaluation',\n",
       " 'Learning Summary Prior Representation for Extractive Summarization',\n",
       " 'A Methodology for Evaluating Timeline Generation Algorithms based on Deep Semantic Units',\n",
       " 'Unsupervised extractive summarization via coverage maximization with syntactic and semantic concepts',\n",
       " 'Low Resource Dependency Parsing: Cross-lingual Parameter Sharing in a Neural Network Parser',\n",
       " 'Semantic Structure Analysis of Noun Phrases using Abstract Meaning Representation',\n",
       " 'Boosting Transition-based AMR Parsing with Refined Actions and Auxiliary Analyzers',\n",
       " 'Generative Incremental Dependency Parsing with Neural Networks',\n",
       " 'Labeled Grammar Induction with Minimal Supervision',\n",
       " 'On the Importance of Ezafe Construction in Persian Parsing',\n",
       " 'Proceedings of the ACL-IJCNLP 2015 Student Research Workshop',\n",
       " 'Unsupervised Learning and Modeling of Knowledge and Intent for Spoken Dialogue Systems',\n",
       " 'Leveraging Compounds to Improve Noun Phrase Translation from Chinese and German',\n",
       " 'Learning Representations for Text-level Discourse Parsing',\n",
       " 'Transition-based Dependency DAG Parsing Using Dynamic Oracles',\n",
       " 'Disease Event Detection based on Deep Modality Analysis',\n",
       " 'Evaluation Dataset and System for Japanese Lexical Simplification',\n",
       " 'Learning to Map Dependency Parses to Abstract Meaning Representations',\n",
       " 'Proceedings of ACL-IJCNLP 2015 System Demonstrations',\n",
       " 'A System Demonstration of a Framework for Computer Assisted Pronunciation Training',\n",
       " 'IMI — A Multilingual Semantic Annotation Environment',\n",
       " 'In-tool Learning for Selective Manual Annotation in Large Corpora',\n",
       " 'KeLP: a Kernel-based Learning Platform for Natural Language Processing',\n",
       " 'Multi-modal Visualization and Search for Text and Prosody Annotations',\n",
       " 'NEED4Tweet: A Twitterbot for Tweets Named Entity Extraction and Disambiguation',\n",
       " 'Visual Error Analysis for Entity Linking',\n",
       " 'A Web-based Collaborative Evaluation Tool for Automatically Learned Relation Extraction Patterns',\n",
       " 'A Dual-Layer Semantic Role Labeling System',\n",
       " 'A system for fine-grained aspect-based sentiment analysis of Chinese',\n",
       " 'Plug Latent Structures and Play Coreference Resolution',\n",
       " 'SCHNÄPPER: A Web Toolkit for Exploratory Relation Extraction',\n",
       " 'OMWEdit - The Integrated Open Multilingual Wordnet Editing System',\n",
       " 'SACRY: Syntax-based Automatic Crossword puzzle Resolution sYstem',\n",
       " 'LEXenstein: A Framework for Lexical Simplification',\n",
       " 'Sharing annotations better: RESTful Open Annotation',\n",
       " 'A Data Sharing and Annotation Service Infrastructure',\n",
       " 'JoBimViz: A Web-based Visualization for Graph-based Distributional Semantic Models',\n",
       " 'End-to-end Argument Generation System in Debating',\n",
       " 'Multi-level Translation Quality Prediction with QuEst++',\n",
       " 'WA-Continuum: Visualising Word Alignments across Multiple Parallel Sentences Simultaneously',\n",
       " 'A Domain-independent Rule-based Framework for Event Extraction',\n",
       " 'Storybase: Towards Building a Knowledge Base for News Events',\n",
       " 'WriteAhead: Mining Grammar Patterns in Corpora for Assisted Writing',\n",
       " 'NiuParser: A Chinese Syntactic and Semantic Parsing Toolkit',\n",
       " 'Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: Tutorial Abstracts',\n",
       " 'Successful Data Mining Methods for NLP',\n",
       " 'Structured Belief Propagation for NLP',\n",
       " 'Sentiment and Belief: How to Think about, Represent, and Annotate Private States',\n",
       " 'Corpus Patterns for Semantic Processing',\n",
       " 'Matrix and Tensor Factorization Methods for Natural Language Processing',\n",
       " 'Scalable Large-Margin Structured Learning: Theory and Algorithms',\n",
       " 'Detecting Deceptive Opinion Spam using Linguistics, Behavioral and Statistical Modeling',\n",
       " 'What You Need to Know about Chinese for Chinese Language Processing',\n",
       " 'Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)',\n",
       " 'Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing',\n",
       " 'Data Recombination for Neural Semantic Parsing',\n",
       " 'Inferring Logical Forms From Denotations',\n",
       " 'Language to Logical Form with Neural Attention',\n",
       " 'Unsupervised Person Slot Filling based on Graph Mining',\n",
       " 'A Multi-media Approach to Cross-lingual Entity Knowledge Transfer',\n",
       " 'Models and Inference for Prefix-Constrained Machine Translation',\n",
       " 'Modeling Coverage for Neural Machine Translation',\n",
       " 'Improving Neural Machine Translation Models with Monolingual Data',\n",
       " 'Graph-Based Translation Via Graph Segmentation',\n",
       " 'Incremental Acquisition of Verb Hypothesis Space towards Physical World Interaction',\n",
       " 'Language Transfer Learning for Supervised Lexical Substitution',\n",
       " 'Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning',\n",
       " 'Pointing the Unknown Words',\n",
       " 'Generalized Transition-based Dependency Parsing via Control Parameters',\n",
       " 'A Transition-Based System for Joint Lexical and Syntactic Analysis',\n",
       " 'Neural Greedy Constituent Parsing with Dynamic Oracles',\n",
       " 'Literal and Metaphorical Senses in Compositional Distributional Semantic Models',\n",
       " 'Idiom Token Classification using Sentential Distributed Semantics',\n",
       " 'Adaptive Joint Learning of Compositional and Non-Compositional Phrase Embeddings',\n",
       " 'Metaphor Detection with Topic Transition, Emotion and Cognition in Context',\n",
       " 'Compressing Neural Language Models by Sparse Word Representations',\n",
       " 'Intrinsic Subspace Evaluation of Word Embedding Representations',\n",
       " 'On the Role of Seed Lexicons in Learning Bilingual Word Embeddings',\n",
       " 'Liberal Event Extraction and Event Schema Induction',\n",
       " 'Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling',\n",
       " 'Using Sentence-Level LSTM Language Models for Script Inference',\n",
       " 'Two Discourse Driven Language Models for Semantics',\n",
       " 'Sentiment Domain Adaptation with Multiple Sources',\n",
       " 'Connotation Frames: A Data-Driven Investigation',\n",
       " 'Bi-Transferring Deep Neural Networks for Domain Adaptation',\n",
       " 'Document-level Sentiment Inference with Social, Faction, and Discourse Context',\n",
       " 'Active Learning for Dependency Parsing with Partial Annotation',\n",
       " 'Dependency Parsing with Bounded Block Degree and Well-nestedness via Lagrangian Relaxation and Branch-and-Bound',\n",
       " 'Query Expansion with Locally-Trained Word Embeddings',\n",
       " 'Together we stand: Siamese Networks for Similar Question Retrieval',\n",
       " 'News Citation Recommendation with Implicit and Explicit Semantics',\n",
       " 'Grapheme-to-Phoneme Models for (Almost) Any Language',\n",
       " 'Neural Word Segmentation Learning for Chinese',\n",
       " 'Transition-Based Neural Word Segmentation',\n",
       " 'A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data',\n",
       " 'Combining Natural Logic and Shallow Reasoning for Question Answering',\n",
       " 'Easy Questions First? A Case Study on Curriculum Learning for Question Answering',\n",
       " 'Improved Representation Learning for Question Answer Matching',\n",
       " 'Tables as Semi-structured Knowledge for Question Answering',\n",
       " 'Neural Summarization by Extracting Sentences and Words',\n",
       " 'Neural Networks For Negation Scope Detection',\n",
       " 'CSE: Conceptual Sentence Embeddings based on Attention Model',\n",
       " 'DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents',\n",
       " 'Investigating the Sources of Linguistic Alignment in Conversation',\n",
       " 'Entropy Converges Between Dialogue Participants: Explanations from an Information-Theoretic Perspective',\n",
       " 'Finding the Middle Ground - A Model for Planning Satisficing Answers',\n",
       " 'A Sentence Interaction Network for Modeling Dependence between Sentences',\n",
       " 'Towards more variation in text generation: Developing and evaluating variation models for choice of referential form',\n",
       " 'How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions',\n",
       " 'Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus',\n",
       " 'Latent Predictor Networks for Code Generation',\n",
       " 'Easy Things First: Installments Improve Referring Expression Generation for Objects in Photographs',\n",
       " 'Collective Entity Resolution with Multi-Focal Attention',\n",
       " 'Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric',\n",
       " 'Improving Coreference Resolution by Learning Entity-Level Distributed Representations',\n",
       " 'Effects of Creativity and Cluster Tightness on Short Text Clustering Performance',\n",
       " 'Generative Topic Embedding: a Continuous Representation of Documents',\n",
       " 'Detecting Common Discussion Topics Across Culture From News Reader Comments',\n",
       " 'A Discriminative Topic Model using Document Network Structure',\n",
       " 'AraSenTi: Large-Scale Twitter-Specific Arabic Sentiment Lexicons',\n",
       " 'Unsupervised Multi-Author Document Decomposition Based on Hidden Markov Model',\n",
       " 'Automatic Text Scoring Using Neural Networks',\n",
       " 'Improved Semantic Parsers For If-Then Statements',\n",
       " 'Universal Dependencies for Learner English',\n",
       " 'Extracting token-level signals of syntactic processing from fMRI - with an application to PoS induction',\n",
       " 'Bidirectional Recurrent Convolutional Neural Network for Relation Classification',\n",
       " 'Sentence Rewriting for Semantic Parsing',\n",
       " 'Chinese Zero Pronoun Resolution with Deep Neural Networks',\n",
       " 'Constrained Multi-Task Learning for Automated Essay Scoring',\n",
       " 'CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases',\n",
       " 'Verbs Taking Clausal and Non-Finite Arguments as Signals of Modality – Revisiting the Issue of Meaning Grounded in Syntax',\n",
       " 'Tree-to-Sequence Attentional Neural Machine Translation',\n",
       " 'Coordination Annotation Extension in the Penn Tree Bank',\n",
       " 'Analyzing Biases in Human Perception of User Age and Gender from Text',\n",
       " 'Modeling Social Norms Evolution for Personalized Sentiment Classification',\n",
       " 'Modeling Concept Dependencies in a Scientific Corpus',\n",
       " 'Normalized Log-Linear Interpolation of Backoff Language Models is Efficient',\n",
       " 'How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation',\n",
       " 'Embeddings for Word Sense Disambiguation: An Evaluation Study',\n",
       " 'Text Understanding with the Attention Sum Reader Network',\n",
       " 'Investigating LSTMs for Joint Extraction of Opinion Entities and Relations',\n",
       " 'Transition-Based Left-Corner Parsing for Identifying PTB-Style Nonlocal Dependencies',\n",
       " 'Siamese CBOW: Optimizing Word Embeddings for Sentence Representations',\n",
       " 'Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings',\n",
       " 'Exploring Convolutional and Recurrent Neural Networks in Sequential Labelling for Dialogue Topic Tracking',\n",
       " 'Cross-Lingual Lexico-Semantic Transfer in Language Learning',\n",
       " 'A CALL System for Learning Preposition Usage',\n",
       " 'A Persona-Based Neural Conversation Model',\n",
       " 'Discriminative Deep Random Walk for Network Classification',\n",
       " 'Normalising Medical Concepts in Social Media Texts by Learning Semantic Representation',\n",
       " 'Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora',\n",
       " 'Deep Fusion LSTMs for Text Semantic Matching',\n",
       " 'Understanding Discourse on Work and Job-Related Well-Being in Public Social Media',\n",
       " 'Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models',\n",
       " 'End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF',\n",
       " 'Off-topic Response Detection for Spontaneous Spoken English Assessment',\n",
       " 'Synthesizing Compound Words for Machine Translation',\n",
       " 'Harnessing Cognitive Features for Sarcasm Detection',\n",
       " 'End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures',\n",
       " 'A short proof that O_2 is an MCFL',\n",
       " 'Context-aware Argumentative Relation Mining',\n",
       " 'Leveraging Inflection Tables for Stemming and Lemmatization.',\n",
       " 'Scaling a Natural Language Generation System',\n",
       " 'ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling',\n",
       " 'Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing',\n",
       " 'Compositional Sequence Labeling Models for Error Detection in Learner Writing',\n",
       " 'Neural Semantic Role Labeling with Dependency Path Embeddings',\n",
       " 'Prediction of Prospective User Engagement with Intelligent Assistants',\n",
       " 'Resolving References to Objects in Photographs using the Words-As-Classifiers Model',\n",
       " 'RBPB: Regularization-Based Pattern Balancing Method for Event Extraction',\n",
       " 'Neural Network-Based Model for Japanese Predicate Argument Structure Analysis',\n",
       " 'Addressing Limited Data for Textual Entailment Across Domains',\n",
       " 'Annotating and Predicting Non-Restrictive Noun Phrase Modifications',\n",
       " 'Bilingual Segmented Topic Model',\n",
       " 'Learning Semantically and Additively Compositional Distributional Representations',\n",
       " 'Inner Attention based Recurrent Neural Networks for Answer Selection',\n",
       " 'Relation Classification via Multi-Level Attention CNNs',\n",
       " 'Knowledge Base Completion via Coupled Path Ranking',\n",
       " 'Larger-Context Language Modelling with Recurrent Neural Network',\n",
       " 'The Creation and Analysis of a Website Privacy Policy Corpus',\n",
       " 'Sequence-based Structured Prediction for Semantic Parsing',\n",
       " 'Learning Word Meta-Embeddings',\n",
       " 'Towards Constructing Sports News from Live Text Commentary',\n",
       " 'A Continuous Space Rule Selection Model for Syntax-based Statistical Machine Translation',\n",
       " 'Probabilistic Graph-based Dependency Parsing with Convolutional Neural Network',\n",
       " 'A Search-Based Dynamic Reranking Model for Dependency Parsing',\n",
       " 'Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning',\n",
       " 'Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields',\n",
       " 'Identifying Causal Relations Using Parallel Wikipedia Articles',\n",
       " 'Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text',\n",
       " 'Commonsense Knowledge Base Completion',\n",
       " 'Simpler Context-Dependent Logical Forms via Model Projections',\n",
       " 'A Fast Unified Model for Parsing and Sentence Understanding',\n",
       " 'Investigating Language Universal and Specific Properties in Word Embeddings',\n",
       " 'Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change',\n",
       " 'Beyond Plain Spatial Knowledge: Determining Where Entities Are and Are Not Located, and For How Long',\n",
       " 'LexSemTm: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning',\n",
       " 'The LAMBADA dataset: Word prediction requiring a broad discourse context',\n",
       " 'WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia',\n",
       " 'Optimizing Spectral Learning for Parsing',\n",
       " 'Stack-propagation: Improved Representation Learning for Syntax',\n",
       " 'Inferring Perceived Demographics from User Emotional Tone and User-Environment Emotional Contrast',\n",
       " 'Prototype Synthesis for Model Laws',\n",
       " 'Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM',\n",
       " 'Discovery of Treatments from Text Corpora',\n",
       " 'Learning Structured Predictors from Bandit Feedback for Interactive NLP',\n",
       " 'Deep Reinforcement Learning with a Natural Language Action Space',\n",
       " 'Incorporating Copying Mechanism in Sequence-to-Sequence Learning',\n",
       " 'Cross-domain Text Classification with Multiple Domains and Disparate Label Sets',\n",
       " 'Morphological Smoothing and Extrapolation of Word Embeddings',\n",
       " 'Cross-lingual Models of Word Embeddings: An Empirical Comparison',\n",
       " 'Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning',\n",
       " 'Minimum Risk Training for Neural Machine Translation',\n",
       " 'A Character-level Decoder without Explicit Segmentation for Neural Machine Translation',\n",
       " 'Target-Side Context for Discriminative Models in Statistical Machine Translation',\n",
       " 'Neural Machine Translation of Rare Words with Subword Units',\n",
       " 'Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network',\n",
       " 'Model Architectures for Quotation Detection',\n",
       " 'Speech Act Modeling of Written Asynchronous Conversations with Task-Specific Embeddings and Conditional Structured Models',\n",
       " 'Situation entity types: automatic classification of clause-level aspect',\n",
       " 'Learning Prototypical Event Structure from Photo Albums',\n",
       " 'Cross-Lingual Image Caption Generation',\n",
       " 'Learning Concept Taxonomies from Multi-modal Data',\n",
       " 'Generating Natural Questions About an Image',\n",
       " 'Physical Causality of Action Verbs in Grounded Language Understanding',\n",
       " 'Optimizing an Approximation of ROUGE - a Problem-Reduction Approach to Extractive Multi-Document Summarization',\n",
       " 'Phrase Structure Annotation and Parsing for Learner English',\n",
       " 'A Trainable Spaced Repetition Model for Language Learning',\n",
       " 'User Modeling in Language Learning with Macaronic Texts',\n",
       " 'On the Similarities Between Native, Non-native and Translated Texts',\n",
       " 'Learning Text Pair Similarity with Context-sensitive Autoencoders',\n",
       " 'Linguistic Benchmarks of Online News Article Quality',\n",
       " 'Alleviating Poor Context with Background Knowledge for Named Entity Disambiguation',\n",
       " 'Mining Paraphrasal Typed Templates from a Plain Text Corpus',\n",
       " 'How to Train Dependency Parsers with Inexact Search for Joint Sentence Boundary Detection and Parsing of Entire Documents',\n",
       " 'MUTT: Metric Unit TesTing for Language Generation Tasks',\n",
       " 'N-gram language models for massively parallel devices',\n",
       " 'Cross-Lingual Morphological Tagging for Low-Resource Languages',\n",
       " 'Semi-Supervised Learning for Neural Machine Translation',\n",
       " 'Strategies for Training Large Vocabulary Neural Language Models',\n",
       " 'Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time',\n",
       " 'Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints',\n",
       " 'Set-Theoretic Alignment for Comparable Corpora',\n",
       " 'Jointly Learning to Embed and Predict with Multiple Languages',\n",
       " 'Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization',\n",
       " 'Efficient techniques for parsing with tree automata',\n",
       " 'A Vector Space for Distributional Semantics for Entailment',\n",
       " 'Hidden Softmax Sequence Model for Dialogue Structure Analysis',\n",
       " 'Summarizing Source Code using a Neural Attention Model',\n",
       " 'Continuous Profile Models in ASL Syntactic Facial Expression Synthesis',\n",
       " 'Evaluating Sentiment Analysis in the Context of Securities Trading',\n",
       " 'Edge-Linear First-Order Dependency Parsing with Undirected Minimum Spanning Tree Inference',\n",
       " 'Topic Extraction from Microblog Posts Using Conversation Structures',\n",
       " 'Neural Relation Extraction with Selective Attention over Instances',\n",
       " 'Leveraging FrameNet to Improve Automatic Event Detection',\n",
       " 'Learning To Use Formulas To Solve Simple Arithmetic Problems',\n",
       " 'Unravelling Names of Fictional Characters',\n",
       " 'Most “babies” are “little” and most “problems” are “huge”: Compositional Entailment in Adjective-Nouns',\n",
       " 'Modeling Stance in Student Essays',\n",
       " 'A New Psychometric-inspired Evaluation Metric for Chinese Word Segmentation',\n",
       " 'Temporal Anchoring of Events for the TimeBank Corpus',\n",
       " 'Grammatical Error Correction: Machine Translation and Classifiers',\n",
       " 'Recurrent neural network models for disease name recognition using domain invariant features',\n",
       " 'Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning',\n",
       " 'A Corpus-Based Analysis of Canonical Word Order of Japanese Double Object Constructions',\n",
       " 'Knowledge-Based Semantic Embedding for Machine Translation',\n",
       " 'One for All: Towards Language Independent Named Entity Linking',\n",
       " 'On Approximately Searching for Similar Word Embeddings',\n",
       " 'Composing Distributed Representations of Relational Patterns',\n",
       " 'The More Antecedents, the Merrier: Resolving Multi-Antecedent Anaphors',\n",
       " 'Automatic Labeling of Topic Models Using Text Summaries',\n",
       " 'Graph-based Dependency Parsing with Bidirectional LSTM',\n",
       " 'TransG : A Generative Model for Knowledge Graph Embedding',\n",
       " 'Question Answering on Freebase via Relation Extraction and Textual Evidence',\n",
       " 'Vector-space topic models for detecting Alzheimer’s disease',\n",
       " 'Chinese Couplet Generation with Neural Network Structures',\n",
       " 'A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task',\n",
       " 'Learning Language Games through Interaction',\n",
       " 'Finding Non-Arbitrary Form-Meaning Systematicity Using String-Metric Learning for Kernel Regression',\n",
       " 'Improving Hypernymy Detection with an Integrated Path-based and Distributional Method',\n",
       " 'Multimodal Pivots for Image Caption Translation',\n",
       " 'Harnessing Deep Neural Networks with Logic Rules',\n",
       " 'Case and Cause in Icelandic: Reconstructing Causal Networks of Cascaded Language Changes',\n",
       " 'On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems',\n",
       " 'Globally Normalized Transition-Based Neural Networks',\n",
       " 'Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)',\n",
       " 'Transition-based dependency parsing with topological fields',\n",
       " 'Scalable Semi-Supervised Query Classification Using Matrix Sketching',\n",
       " 'Learning Multiview Embeddings of Twitter Users',\n",
       " 'Implicit Polarity and Implicit Aspect Recognition in Opinion Mining',\n",
       " 'A Domain Adaptation Regularization for Denoising Autoencoders',\n",
       " 'Incremental Parsing with Minimal Features Using Bi-Directional LSTM',\n",
       " 'Improving Statistical Machine Translation Performance by Oracle-BLEU Model Re-estimation',\n",
       " 'Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings',\n",
       " 'On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models',\n",
       " 'Joint Word Segmentation and Phonetic Category Induction',\n",
       " 'A Language-Independent Neural Network for Event Detection',\n",
       " 'Improved Parsing for Argument-Clusters Coordination',\n",
       " 'Reference Bias in Monolingual Machine Translation Evaluation',\n",
       " 'Cross-lingual projection for class-based language models',\n",
       " 'A Fast Approach for Semantic Similar Short Texts Retrieval',\n",
       " 'Empty element recovery by spinal parser operations',\n",
       " 'Semantic classifications for detection of verb metaphors',\n",
       " 'Recognizing Salient Entities in Shopping Queries',\n",
       " 'Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data',\n",
       " 'Multiplicative Representations for Unsupervised Semantic Role Induction',\n",
       " 'Vocabulary Manipulation for Neural Machine Translation',\n",
       " 'Natural Language Inference by Tree-Based Convolution and Heuristic Matching',\n",
       " 'Improving cross-domain n-gram language modelling with skipgrams',\n",
       " 'Simple PPDB: A Paraphrase Database for Simplification',\n",
       " 'Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning',\n",
       " 'How Naked is the Naked Truth? A Multilingual Lexicon of Nominal Compound Compositionality',\n",
       " 'An Open Web Platform for Rule-Based Speech-to-Sign Translation',\n",
       " 'Word Alignment without NULL Words',\n",
       " 'Unsupervised morph segmentation and statistical language models for vocabulary expansion',\n",
       " 'Detecting Mild Cognitive Impairment by Exploiting Linguistic Information from Transcripts',\n",
       " 'Multi-Modal Representations for Improved Bilingual Lexicon Learning',\n",
       " 'Is This Post Persuasive? Ranking Argumentative Comments in Online Forum',\n",
       " 'The Value of Semantic Parse Labeling for Knowledge Base Question Answering',\n",
       " 'Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification',\n",
       " 'The red one!: On learning to refer to things based on discriminative properties',\n",
       " 'Don’t Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short Text',\n",
       " 'Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model',\n",
       " 'Deep multi-task learning with low level tasks supervised at lower layers',\n",
       " 'Domain Specific Named Entity Recognition Referring to the Real World by Deep Neural Networks',\n",
       " 'An Entity-Focused Approach to Generating Company Descriptions',\n",
       " 'Annotating Relation Inference in Context via Question Answering',\n",
       " 'Automatic Semantic Classification of German Preposition Types: Comparing Hard and Soft Clustering Approaches across Features',\n",
       " 'Natural Language Generation enhances human decision-making with uncertain information',\n",
       " 'Tweet2Vec: Character-Based Distributed Representations for Social Media',\n",
       " 'Phrase-Level Combination of SMT and TM Using Constrained Word Lattice',\n",
       " 'A Neural Network based Approach to Automatic Post-Editing',\n",
       " 'An Unsupervised Method for Automatic Translation Memory Cleaning',\n",
       " 'Exponentially Decaying Bag-of-Words Input Features for Feed-Forward Neural Network in Statistical Machine Translation',\n",
       " 'Syntactically Guided Neural Machine Translation',\n",
       " 'Very quaffable and great fun: Applying NLP to wine reviews',\n",
       " 'Exploring Stylistic Variation with Age and Income on Twitter',\n",
       " 'Finding Optimists and Pessimists on Twitter',\n",
       " 'Transductive Adaptation of Black Box Predictions',\n",
       " 'Which Tumblr Post Should I Read Next?',\n",
       " 'Text Simplification as Tree Labeling',\n",
       " 'Bootstrapped Text-level Named Entity Recognition for Literature',\n",
       " 'The Enemy in Your Own Camp: How Well Can We Detect Statistically-Generated Fake Reviews – An Adversarial Study',\n",
       " 'Character-based Neural Machine Translation',\n",
       " 'Learning Monolingual Compositional Representations via Bilingual Supervision',\n",
       " 'Event Nugget Detection with Forward-Backward Recurrent Neural Networks',\n",
       " 'IBC-C: A Dataset for Armed Conflict Analysis',\n",
       " 'A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings',\n",
       " 'Word Embeddings with Limited Memory',\n",
       " 'Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter',\n",
       " 'Hunting for Troll Comments in News Community Forums',\n",
       " 'Phrase Table Pruning via Submodular Function Maximization',\n",
       " 'Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss',\n",
       " 'Matrix Factorization using Window Sampling and Negative Sampling for Improved Word Representations',\n",
       " 'One model, two languages: training bilingual parsers with harmonized treebanks',\n",
       " 'Using mention accessibility to improve coreference resolution',\n",
       " 'Exploiting Linguistic Features for Sentence Completion',\n",
       " ...]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "with open('ACL_pdfs_all.json','r') as f:\n",
    "    rrr1 = json.load(f)\n",
    "res11 = [j for i in rrr1 for j in rrr1[i]]\n",
    "res11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation',\n",
       " 'Context-Aware Collaborative Topic Regression with Social Matrix Factorization for Recommender Systems',\n",
       " 'Improving Context and Category Matching for Entity Search',\n",
       " 'Machine Translation with Real-Time Web Search',\n",
       " 'Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation',\n",
       " 'Influence Maximization with Novelty Decay in Social Networks',\n",
       " 'CoreCluster: A Degeneracy Based Graph Clustering Framework',\n",
       " 'Experiments on Visual Information Extraction with the Faces of Wikipedia',\n",
       " 'Online Social Spammer Detection',\n",
       " 'User Group Oriented Temporal Dynamics Exploration',\n",
       " 'Predicting Emotions in User-Generated Videos',\n",
       " 'How Long Will It Take? Accurate Prediction of Ontology Reasoning Performance',\n",
       " 'Towards Scalable Exploration of Diagnoses in an Ontology Stream',\n",
       " 'ARIA: Asymmetry Resistant Instance Alignment',\n",
       " 'Learning Parametric Models for Social Infectivity in Multi-Dimensional Hawkes Processes',\n",
       " 'Fraudulent Support Telephone Number Identification Based on Co-Occurrence Information on the Web',\n",
       " 'Compact Aspect Embedding for Diversified Query Expansions',\n",
       " 'Source Free Transfer Learning for Text Classification',\n",
       " 'Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems',\n",
       " 'Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations',\n",
       " 'Combining Heterogenous Social and Geographical Information for Event Recommendation',\n",
       " 'Stochastic Privacy',\n",
       " 'Mapping Users across Networks by Manifold Alignment on Hypergraph',\n",
       " 'Acquiring Comparative Commonsense Knowledge from the Web',\n",
       " 'Who Also Likes It? Generating the Most Persuasive Social Explanations in Recommender Systems',\n",
       " 'Cross-Lingual Knowledge Validation Based Taxonomy Derivation from Heterogeneous Online Wikis',\n",
       " 'Emotion Classification in Microblog Texts Using Class Sequential Rules',\n",
       " 'Quality-Based Learning for Web Data Classification',\n",
       " 'Cross-View Feature Learning for Scalable Social Image Analysis',\n",
       " 'Capturing Difficulty Expressions in Student Online Q&A Discussions',\n",
       " 'A Joint Optimization Model for Image Summarization Based on Image Content and Tags',\n",
       " 'Recommendation by Mining Multiple User Behaviors with Group Sparsity',\n",
       " 'Learning Temporal Dynamics of Behavior Propagation in Social Networks',\n",
       " 'Trust Prediction with Propagation and Similarity Regularization',\n",
       " 'Synthesis of Geometry Proof Problems',\n",
       " 'GenEth: A General Ethical Dilemma Analyzer',\n",
       " 'Huffman Coding for Storing Non-Uniformly Distributed Messages in Networks of Neural Cliques',\n",
       " 'Where and Why Users \"Check In\"',\n",
       " 'A Machine Learning Approach to Musically Meaningful Homogeneous Style Classification',\n",
       " 'Programming by Example Using Least General Generalizations',\n",
       " 'Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes',\n",
       " 'Joule Counting Correction for Electric Vehicles Using Artificial Neural Networks',\n",
       " 'How Do Your Friends on Social Media Disclose Your Emotions?',\n",
       " 'Forecasting Potential Diabetes Complications',\n",
       " 'k-CoRating: Filling Up Data to Obtain Privacy and Utility',\n",
       " 'Modeling Subjective Experience-Based Learning under Uncertainty and Frames',\n",
       " 'The Importance of Cognition and Affect for Artificially Intelligent Decision Makers',\n",
       " 'Efficient Codes for Inverse Dynamics During Walking',\n",
       " 'An Agent-Based Model Studying the Acquisition of a Language System of Logical Constructions',\n",
       " 'Large-Scale Analogical Reasoning',\n",
       " 'Learning Compositional Sparse Models of Bimodal Percepts',\n",
       " 'Using Narrative Function to Extract Qualitative Information from Natural Language Texts',\n",
       " \"Confident Reasoning on Raven's Progressive Matrices Tests\",\n",
       " 'Learning Goal-Oriented Hierarchical Tasks from Situated Interactive Instruction',\n",
       " 'Learning Unknown Event Models',\n",
       " \"Social Planning: Achieving Goals by Altering Others' Mental States\",\n",
       " 'Spatio-Temporal Consistency as a Means to Identify Unlabeled Objects in a Continuous Data Field',\n",
       " 'Placement of Loading Stations for Electric Vehicles: No Detours Necessary!',\n",
       " 'A Region-Based Model for Estimating Urban Air Pollution',\n",
       " 'Spatial Scan for Disease Mapping on a Mobile Population',\n",
       " 'Challenges in Materials Discovery — Synthetic Generator and Real Datasets',\n",
       " 'Supervised Scoring with Monotone Multidimensional Splines',\n",
       " 'Efficient Buyer Groups for Prediction-of-Use Electricity Tariffs',\n",
       " 'Intelligent System for Urban Emergency Management during Large-Scale Disaster',\n",
       " \"TacTex'13: A Champion Adaptive Power Trading Agent\",\n",
       " 'Effective Management of Electric Vehicle Storage Using Smart Charging',\n",
       " 'Rounded Dynamic Programming for Tree-Structured Stochastic Network Design',\n",
       " 'Contextually Supervised Source Separation with Application to Energy Disaggregation',\n",
       " 'Modeling and Mining Spatiotemporal Patterns of Infection Risk from Heterogeneous Data for Active Surveillance Planning',\n",
       " 'A Latent Variable Model for Discovering Bird Species Commonly Misidentified by Citizen Scientists',\n",
       " 'Approximate Equilibrium and Incentivizing Social Coordination',\n",
       " 'Solving the Inferential Frame Problem in the General Game Description Language',\n",
       " 'Generating Content for Scenario-Based Serious-Games Using CrowdSourcing',\n",
       " 'Automatic Game Design via Mechanic Generation',\n",
       " 'False-Name Bidding and Economic Efficiency in Combinatorial Auctions',\n",
       " 'On the Incompatibility of Efficiency and Strategyproofness in Randomized Social Choice',\n",
       " 'Fixing a Balanced Knockout Tournament',\n",
       " 'A Generalization of Probabilistic Serial to Randomized Social Choice',\n",
       " 'Simultaneous Cake Cutting',\n",
       " 'Lazy Defenders Are Almost Optimal against Diligent Attackers',\n",
       " 'Extending Tournament Solutions',\n",
       " 'The Fisher Market Game: Equilibrium and Welfare',\n",
       " 'Regret Transfer and Parameter Optimization',\n",
       " 'Solving Imperfect Information Games Using Decomposition',\n",
       " 'Biased Games',\n",
       " 'Modal Ranking: A Uniquely Robust Voting Rule',\n",
       " 'Mechanism Design for Scheduling with Uncertain Execution Time',\n",
       " 'Using Response Functions to Measure Strategy Strength',\n",
       " 'New Models for Competitive Contagion',\n",
       " 'Preference Elicitation and Interview Minimization in Stable Matchings',\n",
       " 'A Characterization of the Single-Peaked Single-Crossing Domain',\n",
       " 'On Detecting Nearly Structured Preference Profiles',\n",
       " 'Binary Aggregation by Selection of the Most Representative Voters',\n",
       " 'On the Axiomatic Characterization of Runoff Voting Rules',\n",
       " \"Potential-Aware Imperfect-Recall Abstraction with Earth Mover's Distance in Imperfect-Information Games\",\n",
       " 'Mechanism Design for Mobile Geo-Location Advertising',\n",
       " 'Voting with Rank Dependent Scoring Rules',\n",
       " 'Increasing VCG Revenue by Decreasing the Quality of Items',\n",
       " 'A Control Dichotomy for Pure Scoring Rules',\n",
       " 'A Multiarmed Bandit Incentive Mechanism for Crowdsourcing Demand Response in Smart Grids',\n",
       " 'Envy-Free Division of Sellable Goods',\n",
       " 'Betting Strategies, Market Selection, and the Wisdom of Crowds',\n",
       " 'Incomplete Preferences in Single-Peaked Electorates',\n",
       " 'Item Bidding for Combinatorial Public Projects',\n",
       " 'Regret-Based Optimization and Preference Elicitation for Stackelberg Security Games with Uncertainty',\n",
       " 'On the Structure of Synergies in Cooperative Games',\n",
       " 'Incentives for Truthful Information Elicitation of Continuous Signals',\n",
       " 'Equilibria in Epidemic Containment Games',\n",
       " 'Bounding the Support Size in Extensive Form Games with Imperfect Information',\n",
       " 'Two Case Studies for Trading Multiple Indivisible Goods with Indifferences',\n",
       " 'Beat the Cheater: Computing Game-Theoretic Strategies for When to Kick a Gambler out of a Casino',\n",
       " 'Strategyproof Exchange with Multiple Private Endowments',\n",
       " 'A Strategy-Proof Online Auction with Time Discounting Values',\n",
       " 'Incentivizing High-Quality Content from Heterogeneous Users: On the Existence of Nash Equilibrium',\n",
       " 'Game-Theoretic Resource Allocation for Protecting Large Public Events',\n",
       " 'Relaxation Search: A Simple Way of Managing Optional Clauses',\n",
       " 'Parallel Restarted Search',\n",
       " 'Designing Fast Absorbing Markov Chains',\n",
       " 'Simpler Bounded Suboptimal Search',\n",
       " 'Elimination Ordering in Lifted First-Order Probabilistic Inference',\n",
       " 'Exponential Deepening A* for Real-Time Agent-Centered Search',\n",
       " 'Identifying Hierarchies for Fast Optimal Search',\n",
       " 'Worst-Case Solution Quality Analysis When Not Re-Expanding Nodes in Best-First Search',\n",
       " 'Sparse Learning for Stochastic Composite Optimization',\n",
       " 'Acquiring Commonsense Knowledge for Sentiment Analysis through Human Computation',\n",
       " 'Signals in the Silence: Models of Implicit Feedback in a Recommendation System for Crowdsourcing',\n",
       " 'Leveraging Fee-Based, Imperfect Advisors in Human-Agent Games of Trust',\n",
       " \"Can Agent Development Affect Developer's Strategy?\",\n",
       " 'Ordering Effects and Belief Adjustment in the Use of Comparison Shopping Agents',\n",
       " 'A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback',\n",
       " 'Dramatis: A Computational Model of Suspense',\n",
       " 'Sketch Recognition with Natural Correction and Editing',\n",
       " 'Role-Aware Conformity Modeling and Analysis in Social Networks',\n",
       " 'Managing Change in Graph-Structured Data Using Description Logics',\n",
       " 'The Computational Complexity of Structure-Based Causality',\n",
       " 'Pathway Specification and Comparative Queries: A High Level Language with Petri Net Semantics',\n",
       " 'PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains',\n",
       " 'Querying Inconsistent Description Logic Knowledge Bases under Preferred Repair Semantics',\n",
       " 'Capturing Relational Schemas and Functional Dependencies in RDFS',\n",
       " 'Exploring the Boundaries of Decidable Verification of Non-Terminating Golog Programs',\n",
       " 'Data Quality in Ontology-based Data Access: The Case of Consistency',\n",
       " 'Reasoning on LTL on Finite Traces: Insensitivity to Infiniteness',\n",
       " 'A Tractable Approach to ABox Abduction over Description Logic Ontologies',\n",
       " 'Exploiting Support Sets for Answer Set Programs with External Evaluations',\n",
       " 'A Knowledge Compilation Map for Ordered Real-Valued Decision Diagrams',\n",
       " 'The Complexity of Reasoning with FODD and GFODD',\n",
       " 'Elementary Loops Revisited',\n",
       " 'A Constructive Argumentation Framework',\n",
       " 'Datalog Rewritability of Disjunctive Datalog Programs and its Applications to Ontology Reasoning',\n",
       " 'Qualitative Reasoning with Modelica Models',\n",
       " 'A Parameterized Complexity Analysis of Generalized CP-Nets',\n",
       " 'The Most Uncreative Examinee: A First Step toward Wide Coverage Natural Language Math Problem Solving',\n",
       " 'Computing General First-Order Parallel and Prioritized Circumscription',\n",
       " 'Knowledge Graph Embedding by Translating on Hyperplanes',\n",
       " 'Abduction Framework for Repairing Incomplete EL Ontologies: Complexity Results and Algorithms',\n",
       " 'Local-to-Global Consistency Implies Tractability of Abduction',\n",
       " 'Using Model-Based Diagnosis to Improve Software Testing',\n",
       " 'Pay-As-You-Go OWL Query Answering Using a Triple Store',\n",
       " 'Contraction and Revision over DL-Lite TBoxes',\n",
       " 'A Spatially Sensitive Kernel to Predict Cognitive Performance from Short-Term Changes in Neural Structure',\n",
       " 'Accurate Household Occupant Behavior Modeling Based on Data Mining Techniques',\n",
       " 'A Convex Formulation for Semi-Supervised Multi-Label Feature Selection',\n",
       " 'Predicting Postoperative Atrial Fibrillation from Independent ECG Components',\n",
       " 'Online Portfolio Selection with Group Sparsity',\n",
       " 'Latent Low-Rank Transfer Subspace Learning for Missing Modality Recognition',\n",
       " 'On the Challenges of Physical Implementations of RBMs',\n",
       " 'SOML: Sparse Online Metric Learning with Application to Image Retrieval',\n",
       " 'Calibration-Free BCI Based Control',\n",
       " 'User Intent Identification from Online Discussions Using a Joint Aspect-Action Topic Model',\n",
       " 'Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery',\n",
       " 'Ranking Tweets by Labeled and Collaboratively Selected Pairs with Transitive Closure',\n",
       " 'Automatic Construction and Natural-Language Description of Nonparametric Regression Models',\n",
       " 'Proximal Iteratively Reweighted Algorithm with Multiple Splitting for Nonconvex Sparsity Optimization',\n",
       " 'Direct Semantic Analysis for Social Image Classification',\n",
       " 'Discovering Better AAAI Keywords via Clustering with Community-Sourced Constraints',\n",
       " 'Learning Latent Engagement Patterns of Students in Online Courses',\n",
       " 'Generalized Higher-Order Tensor Decomposition via Parallel ADMM',\n",
       " 'Doubly Regularized Portfolio with Risk Minimization',\n",
       " 'Learning Deep Representations for Graph Clustering',\n",
       " 'Agent Behavior Prediction and Its Generalization Analysis',\n",
       " 'Evaluating Trauma Patients: Addressing Missing Covariates with Joint Optimization',\n",
       " 'Identifying Differences in Physician Communication Styles with a Log-Linear Transition Component Model',\n",
       " 'Robust Distance Metric Learning in the Presence of Label Noise',\n",
       " 'Globally and Locally Consistent Unsupervised Projection',\n",
       " 'Adaptive Knowledge Transfer for Multiple Instance Learning in Image Classification',\n",
       " 'Privacy and Regression Model Preserved Learning',\n",
       " 'Decomposing Activities of Daily Living to Discover Routine Clusters',\n",
       " 'Feature Selection at the Discrete Limit',\n",
       " 'Hybrid Singular Value Thresholding for Tensor Completion',\n",
       " 'Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks',\n",
       " 'Accurate Integration of Aerosol Predictions by Smoothing on a Manifold',\n",
       " 'Dynamic Multi-Agent Task Allocation with Spatial and Temporal Constraints',\n",
       " 'Robust Winners and Winner Determination Policies under Candidate Uncertainty',\n",
       " 'Prices Matter for the Parameterized Complexity of Shift Bribery',\n",
       " 'The Computational Rise and Fall of Fairness',\n",
       " 'Multi-Organ Exchange: The Whole Is Greater than the Sum of its Parts',\n",
       " 'On Computing Optimal Strategies in Open List Proportional Representation: The Two Parties Case',\n",
       " 'Symbolic Model Checking Epistemic Strategy Logic',\n",
       " 'Internally Stable Matchings and Exchanges',\n",
       " 'Congestion Games for V2G-Enabled EV Charging',\n",
       " 'Decentralized Multi-Agent Reinforcement Learning in Average-Reward Dynamic DCOPs',\n",
       " 'Online (Budgeted) Social Choice',\n",
       " 'A Game-Theoretic Analysis of Catalog Optimization',\n",
       " 'Theory of Cooperation in Complex Social Networks',\n",
       " 'Multiagent Metareasoning through Organizational Design',\n",
       " 'Give a Hard Problem to a Diverse Team: Exploring Large Action Spaces',\n",
       " 'Regret-Based Multi-Agent Coordination with Uncertain Task Rewards',\n",
       " 'Solving Zero-Sum Security Games in Discretized Spatio-Temporal Domains',\n",
       " 'Scalable Complex Contract Negotiation with Structured Search and Agenda Management',\n",
       " 'SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis',\n",
       " 'Joint Morphological Generation and Syntactic Linearization',\n",
       " 'Improving Domain-independent Cloud-Based Speech Recognition with Domain-Dependent Phonetic Post-Processing',\n",
       " 'Adaptive Multi-Compositionality for Recursive Neural Models with Applications to Sentiment Analysis',\n",
       " 'Collaborative Models for Referring Expression Generation in Situated Dialogue',\n",
       " 'Prediction of Helpful Reviews Using Emotions Extraction',\n",
       " 'Unsupervised Alignment of Natural Language Instructions with Video Segments',\n",
       " 'Learning Scripts as Hidden Markov Models',\n",
       " 'Learning Word Representation Considering Proximity and Ambiguity',\n",
       " 'On Dataless Hierarchical Text Classification',\n",
       " 'Learning Concept Embeddings for Query Expansion by Quantum Entropy Minimization',\n",
       " 'Fused Feature Representation Discovery for High-Dimensional and Sparse Data',\n",
       " 'Instance-Based Domain Adaptation in NLP via In-Target-Domain Logistic Approximation',\n",
       " 'Semi-Supervised Matrix Completion for Cross-Lingual Text Classification',\n",
       " 'Chinese Overt Pronoun Resolution: A Bilingual Approach',\n",
       " 'Chinese Zero Pronoun Resolution: An Unsupervised Approach Combining Ranking and Integer Linear Programming',\n",
       " 'Extracting Keyphrases from Research Papers Using Citation Networks',\n",
       " 'SUIT: A Supervised User-Item Based Topic Model for Sentiment Analysis',\n",
       " 'Lifetime Lexical Variation in Social Media',\n",
       " 'Detecting Information-Dense Texts in Multiple News Domains',\n",
       " 'Mind the Gap: Machine Translation by Minimizing the Semantic Gap in Embedding Space',\n",
       " 'Supervised Transfer Sparse Coding',\n",
       " 'Active Learning with Model Selection',\n",
       " 'Multilabel Classification with Label Correlations and Missing Labels',\n",
       " 'Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence',\n",
       " 'Optimal Neighborhood Preserving Visualization by Maximum Satisfiability',\n",
       " 'PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences',\n",
       " 'Manifold Spanning Graphs',\n",
       " 'LASS: A Simple Assignment Model with Laplacian Smoothing',\n",
       " 'Distribution-Aware Sampling and Weighted Model Counting for SAT',\n",
       " 'Dynamic Bayesian Probabilistic Matrix Factorization',\n",
       " 'Echo-State Conditional Restricted Boltzmann Machines',\n",
       " 'A Local Non-Negative Pursuit Method for Intrinsic Manifold Structure Preservation',\n",
       " 'Dropout Training for Support Vector Machines',\n",
       " 'Learning with Augmented Class by Exploiting Unlabeled Data',\n",
       " 'Natural Temporal Difference Learning',\n",
       " 'Learning the Structure of Probabilistic Graphical Models with an Extended Cascading Indian Buffet Process',\n",
       " \"Finding Median Point-Set Using Earth Mover's Distance\",\n",
       " 'Non-Linear Label Ranking for Large-Scale Prediction of Long-Term User Interests',\n",
       " 'HC-Search for Multi-Label Prediction: An Empirical Study',\n",
       " 'Learning Instance Concepts from Multiple-Instance Data with Bags as Distributions',\n",
       " 'Active Learning for Crowdsourcing Using Knowledge Transfer',\n",
       " 'Large-Scale Optimistic Adaptive Submodularity',\n",
       " 'Coactive Learning for Locally Optimal Problem Solving',\n",
       " 'Kernelized Bayesian Transfer Learning',\n",
       " 'ReLISH: Reliable Label Inference via Smoothness Hypothesis',\n",
       " 'Signed Laplacian Embedding for Supervised Dimension Reduction',\n",
       " 'Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework',\n",
       " 'Deep Modeling of Group Preferences for Group-Based Recommendation',\n",
       " 'Fast Multi-Instance Multi-Label Learning',\n",
       " 'Adaptation-Guided Case Base Maintenance',\n",
       " 'Intra-View and Inter-View Supervised Correlation Analysis for Multi-View Feature Learning',\n",
       " 'Imitation Learning with Demonstrations and Shaping Rewards',\n",
       " 'Monte Carlo Filtering Using Kernel Embedding of Distributions',\n",
       " 'Power Iterated Color Refinement',\n",
       " 'Spectral Thompson Sampling',\n",
       " 'Non-Convex Feature Learning via L_{p, inf} Operator',\n",
       " 'Pairwise-Covariance Linear Discriminant Analysis',\n",
       " 'Constructing Symbolic Representations for High-Level Planning',\n",
       " 'Feature-Cost Sensitive Learning with Submodular Trees of Classifiers',\n",
       " 'Scalable Sparse Covariance Estimation via Self-Concordance',\n",
       " 'Wormhole Hamiltonian Monte Carlo',\n",
       " 'Manifold Learning for Jointly Modeling Topic and Visualization',\n",
       " 'Partial Multi-View Clustering',\n",
       " 'Sample-Adaptive Multiple Kernel Learning',\n",
       " 'Pre-Trained Multi-View Word Embedding Using Two-Side Neural Network',\n",
       " 'Convex Co-embedding',\n",
       " 'Mixing-Time Regularized Policy Gradient',\n",
       " 'Semantic Data Representation for Improving Tensor Factorization',\n",
       " 'Labeling Complicated Objects: Multi-View Multi-Instance Multi-Label Learning',\n",
       " 'Online and Stochastic Learning with a Human Cognitive Bias',\n",
       " 'Robust Non-Negative Dictionary Learning',\n",
       " 'Evolutionary Dynamics of Q-Learning over the Sequence Form',\n",
       " 'Bagging by Design (on the Suboptimality of Bagging)',\n",
       " 'Anytime Active Learning',\n",
       " 'On Boosting Sparse Parities',\n",
       " 'Online Multi-Task Learning via Sparse Dictionary Optimization',\n",
       " 'A Hybrid Grammar-Based Approach for Learning and Recognizing Natural Hand Gestures',\n",
       " 'Sparse Compositional Metric Learning',\n",
       " 'Locality Preserving Projection for Domain Adaptation with Multi-Objective Learning',\n",
       " 'Reconsidering Mutual Information Based Feature Selection: A Statistical Significance View',\n",
       " 'Cross-Domain Metric Learning Based on Information Theory',\n",
       " 'Improving Semi-Supervised Target Alignment via Label-Aware Base Kernels',\n",
       " 'Exact Subspace Clustering in Linear Time',\n",
       " 'Using The Matrix Ridge Approximation to Speedup Determinantal Point Processes Sampling Algorithms',\n",
       " 'The Role of Dimensionality Reduction in Classification',\n",
       " 'Small-Variance Asymptotics for Dirichlet Process Mixtures of SVMs',\n",
       " 'Learning Relative Similarity by Stochastic Dual Coordinate Ascent',\n",
       " 'Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition',\n",
       " 'Supervised Hashing for Image Retrieval via Image Representation Learning',\n",
       " 'Efficient Generalized Fused Lasso and its Application to the Diagnosis of Alzheimer’s Disease',\n",
       " 'Online Classification Using a Voted RDA Method',\n",
       " 'Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization',\n",
       " 'Multi-Instance Learning with Distribution Change',\n",
       " 'Novel Density-Based Clustering Algorithms for Uncertain Data',\n",
       " 'Robust Bayesian Inverse Reinforcement Learning with Sparse Behavior Noise',\n",
       " 'Gradient Descent with Proximal Average for Nonconvex and Composite Regularization',\n",
       " 'Hybrid Heterogeneous Transfer Learning through Deep Learning',\n",
       " 'Oversubscription Planning: Complexity and Compilability',\n",
       " 'Planning as Model Checking in Hybrid Domains',\n",
       " 'Flexible and Scalable Partially Observable Planning with Linear Translations',\n",
       " 'Using Timed Game Automata to Synthesize Execution Strategies for Simple Temporal Networks with Uncertainty',\n",
       " 'Scheduling for Transfers in Pickup and Delivery Problems with Very Large Neighborhood Search',\n",
       " 'Structured Possibilistic Planning Using Decision Diagrams',\n",
       " 'Chance-Constrained Probabilistic Simple Temporal Problems',\n",
       " 'Solving the Traveling Tournament Problem by Packing Three-Vertex Paths',\n",
       " 'Delivering Guaranteed Display Ads under Reach and Frequency Requirements',\n",
       " 'Solving Uncertain MDPs by Reusing State Information and Plans',\n",
       " 'Grandpa Hates Robots - Interaction Constraints for Planning in Inhabited Environments',\n",
       " 'Backdoors to Planning',\n",
       " 'A Simple Polynomial-Time Randomized Distributed Algorithm for Connected Row Convex Constraints',\n",
       " 'Symbolic Domain Predictive Control',\n",
       " 'Computing Contingent Plans via Fully Observable Non-Deterministic Planning',\n",
       " 'A Scheduler for Actions with Iterated Durations',\n",
       " 'Parametrized Families of Hard Planning Problems from Phase Transitions',\n",
       " 'Cost-Based Query Optimization via AI Planning',\n",
       " 'Efficiently Implementing GOLOG with Answer Set Programming',\n",
       " 'Generalized Label Reduction for Merge-and-Shrink Heuristics',\n",
       " 'Saturated Path-Constrained MDP: Planning under Uncertainty and Deterministic Model-Checking Constraints',\n",
       " 'A Relevance-Based Compilation Method for Conformant Probabilistic Planning',\n",
       " 'Optimal Decoupling in Linear Constraint Systems',\n",
       " 'Adding Local Exploration to Greedy Best-First Search in Satisficing Planning',\n",
       " 'Type-Based Exploration with Multiple Search Queues for Satisficing Planning',\n",
       " 'Lifting Relational MAP-LPs Using Cluster Signatures',\n",
       " 'Recovering from Selection Bias in Causal and Statistical Inference',\n",
       " 'Tree-Based On-Line Reinforcement Learning',\n",
       " 'Testable Implications of Linear Structural Equation Models',\n",
       " 'Finding the k-best Equivalence Classes of Bayesian Network Structures for Model Averaging',\n",
       " 'Tightening Bounds for Bayesian Network Structure Learning',\n",
       " 'State Aggregation in Monte Carlo Tree Search',\n",
       " 'Relational One-Class Classification: A Non-Parametric Approach',\n",
       " 'Predicting the Hardness of Learning Bayesian Networks',\n",
       " 'Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference',\n",
       " 'R2: An Efficient MCMC Sampler for Probabilistic Programs',\n",
       " 'An Adversarial Interpretation of Information-Theoretic Bounded Rationality',\n",
       " 'Explanation-Based Approximate Weighted Model Counting for Probabilistic Logics',\n",
       " 'Approximate Lifting Techniques for Belief Propagation',\n",
       " 'Decentralized Stochastic Planning with Anonymity in Interactions',\n",
       " 'Point-Based POMDP Solving with Factored Value Function Approximation',\n",
       " 'Efficient Optimization for Autonomous Robotic Manipulation of Natural Objects',\n",
       " 'A Framework for Task Planning in Heterogeneous Multi Robot Systems Based on Robot Capabilities',\n",
       " 'Generalizing Policy Advice with Gaussian Process Bandits for Dynamic Skill Improvement',\n",
       " 'Minimising Undesired Task Costs in Multi-Robot Task Allocation Problems with In-Schedule Dependencies',\n",
       " 'Optimal and Efficient Stochastic Motion Planning in Partially-Known Environments',\n",
       " 'Learning from Unscripted Deictic Gesture and Language for Human-Robot Interactions',\n",
       " 'Robust Visual Robot Localization Across Seasons Using Network Flows',\n",
       " 'Schedule-Based Robotic Search for Multiple Residents in a Retirement Home Environment',\n",
       " 'Qualitative Planning with Quantitative Constraints for Online Learning of Robotic Behaviours',\n",
       " 'GP-Localize: Persistent Mobile Robot Localization Using Online Sparse Gaussian Process Observation Model',\n",
       " 'MaxSAT by Improved Instance-Specific Algorithm Configuration',\n",
       " 'Adaptive Singleton-Based Consistencies',\n",
       " 'Non-Restarting SAT Solvers with Simple Preprocessing Can Efficiently Simulate Resolution',\n",
       " 'Propagating Regular Counting Constraints',\n",
       " 'Tailoring Local Search for Partial MaxSAT',\n",
       " 'Q-Intersection Algorithms for Constraint-Based Robust Parameter Estimation',\n",
       " 'Linear-Time Filtering Algorithms for the Disjunctive Constraint',\n",
       " 'Diagnosing Analogue Linear Systems Using Dynamic Topological Reconfiguration',\n",
       " 'Backdoors into Heterogeneous Classes of SAT and CSP',\n",
       " 'A Reasoner for the RCC-5 and RCC-8 Calculi Extended with Constants',\n",
       " 'An Experimentally Efficient Method for (MSS, CoMSS) Partitioning',\n",
       " 'A Support-Based Algorithm for the Bi-Objective Pareto Constraint',\n",
       " 'DJAO: A Communication-Constrained DCOP Algorithm that Combines Features of ADOPT and Action-GDL',\n",
       " 'Preprocessing for Propositional Model Counting',\n",
       " 'Boosting SBDS for Partial Symmetry Breaking in Constraint Programming',\n",
       " 'Double Configuration Checking in Stochastic Local Search for Satisfiability',\n",
       " 'A Propagator Design Framework for Constraints over Sequences',\n",
       " 'Maximum Satisfiability Using Core-Guided MaxSAT Resolution',\n",
       " 'Fast Consistency Checking of Very Large Real-World RCC-8 Constraint Networks Using Graph Partitioning',\n",
       " 'Avoiding Plagiarism in Markov Sequence Generation',\n",
       " 'Cached Iterative Weakening for Optimal Multi-Way Number Partitioning',\n",
       " 'Exploiting Competition Relationship for Robust Visual Recognition',\n",
       " 'Towards Topological-Transformation Robust Shape Comparison: A Sparse Representation Based Manifold Embedding Approach',\n",
       " 'Grounding Acoustic Echoes in Single View Geometry Estimation',\n",
       " 'Similarity-Preserving Binary Signature for Linear Subspaces',\n",
       " 'Deep Salience: Visual Salience Modeling via Deep Belief Propagation',\n",
       " 'Locality-Constrained Low-Rank Coding for Image Classification',\n",
       " 'Uncorrelated Multi-View Discrimination Dictionary Learning for Recognition',\n",
       " 'Learning to Recognize Novel Objects in One Shot through Human-Robot Interactions in Natural Language Dialogues',\n",
       " 'Sub-Selective Quantization for Large-Scale Image Search',\n",
       " 'Learning Low-Rank Representations with Classwise Block-Diagonal Structure for Robust Face Recognition',\n",
       " 'Efficient Object Detection via Adaptive Online Selection of Sensor-Array Elements',\n",
       " 'On Hair Recognition in the Wild by Machine',\n",
       " 'Diagram Understanding in Geometry Questions',\n",
       " 'A Generalized Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles of Complex Types',\n",
       " 'Low-Rank Tensor Completion with Spatio-Temporal Consistency',\n",
       " 'Semantic Graph Construction for Weakly-Supervised Image Parsing',\n",
       " 'Latent Domains Modeling for Visual Domain Adaptation',\n",
       " 'Semantic Segmentation Using Multiple Graphs with Block-Diagonal Constraints',\n",
       " 'Locality Preserving Hashing',\n",
       " 'Predictive Models for Determining If and When to Display Online Lead Forms',\n",
       " 'Engineering Works Scheduling for Hong Kong’s Rail Network',\n",
       " 'THink: Inferring Cognitive Status from Subtle Behaviors',\n",
       " 'The Quest Draft: An Automated Course Allocation Algorithm',\n",
       " 'Evaluation and Deployment of a People-to-People Recommender in Online Dating',\n",
       " 'Deploying CommunityCommands: A Software Command Recommender System Case Study',\n",
       " 'CiteSeerX: AI in a Digital Library Search Engine',\n",
       " 'Pattern Discovery in Protein Networks Reveals High-Confidence Predictions of Novel Interactions',\n",
       " 'Crowdsourcing for Multiple-Choice Question Answering',\n",
       " 'Advice Provision for Energy Saving in Automobile Climate Control Systems',\n",
       " 'A Smart Range Helping Cognitively-Impaired Persons Cooking',\n",
       " 'STREETS: Game-Theoretic Traffic Patrolling with Exploration and Exploitation',\n",
       " 'Swissnoise: Online Polls with Game-Theoretic Incentives',\n",
       " 'Robust Protection of Fisheries with COmPASS',\n",
       " 'Optimizing a Start-Stop Controller Using Policy Search',\n",
       " 'A Unified Framework for Augmented Reality and Knowledge-Based Systems in Maintaining Aircraft',\n",
       " 'A Schedule Optimization Tool for Destructive and Non-Destructive Vehicle Tests',\n",
       " 'AI-MIX: Using Automated Planning to Steer Human Workers Towards Better Crowdsourced Plans',\n",
       " 'A Speech-Driven Second Screen Application for TV Program Discovery',\n",
       " 'Clustering Species Accumulation Curves to Identify Skill Levels of Citizen Scientists Participating in the eBird Project',\n",
       " 'StrokeBank: Automating Personalized Chinese Handwriting Generation',\n",
       " 'DOROTHY: Enhancing Bidirectional Communication between a 3D Programming Interface and Mobile Robots',\n",
       " 'Shallow Blue: Lego-Based Embodied AI as a Platform for Cross-Curricular Project Based Learning',\n",
       " 'Teaching With Watson',\n",
       " 'Jim: A Platform for Affective AI in an Interdisciplinary Setting',\n",
       " 'Easychair as a Pedagogical Tool: Engaging Graduate Students in the Reviewing Process',\n",
       " 'Model AI Assignments 2014',\n",
       " 'Making CP-Nets (More) Useful',\n",
       " 'Information Sharing for Care Coordination',\n",
       " 'The Effect of Similarity between Human and Machine Action Choices on Adaptive Automation Performance',\n",
       " 'Solving Semantic Problems Using Contexts Extracted from Knowledge Graphs',\n",
       " 'Reinforcement Learning on Multiple Correlated Signals',\n",
       " 'Analogy Tutor: A Tutoring System for Promoting Conceptual Learning via Comparison',\n",
       " 'Imputation, Social Choice, and Partial Preferences',\n",
       " 'Robot Team Exploration with Communication Restrictions',\n",
       " 'The Semantic Interpretation of Trust in Multiagent Interactions',\n",
       " 'Modeling Argumentation and Explanation in the Social Web',\n",
       " 'Automatically Creating Multilingual Lexical Resources',\n",
       " 'Probabilistic Planning with Reduced Models',\n",
       " 'Roles and Teams Hedonic Games',\n",
       " 'Compilation Based Approaches to Probabilistic Planning -- Thesis Summary',\n",
       " 'Living and Searching in the World: Object-Based State Estimation for Mobile Robots',\n",
       " 'Optimizing and Learning Diffusion Behaviors in Complex Network',\n",
       " 'Social Capital in Network Organizations',\n",
       " 'To Share or Not to Share? The Single Agent in a Team Decision Problem',\n",
       " 'Monte-Carlo Simulation Adjusting',\n",
       " 'Advice Provision for Choice Selection Processes with Ranked Options',\n",
       " 'A Knowledge Representation that Models Memory in Narrative Comprehension',\n",
       " 'Association Rule Hiding Based on Evolutionary Multi-Objective Optimization by Removing Items',\n",
       " 'A Model for Aggregating Contributions of Synergistic Crowdsourcing Workflows',\n",
       " 'Online Search Algorithm Configuration',\n",
       " 'Addressing Complexity in Multi-Issue Negotiation via Utility Hypergraphs',\n",
       " 'Communication-Restricted Exploration for Small Teams',\n",
       " 'Genotypic versus Behavioural Diversity for Teams of Programs under the 4-v-3 Keepaway Soccer Task',\n",
       " 'A Novel Single-DBN Generative Model for Optimizing POMDP Controllers by Probabilistic Inference',\n",
       " 'Partial Satisfaction Planning under Time Uncertainty with Control on When Objectives Can Be Aborted',\n",
       " 'Semantical Clustering of Morphologically Related Chinese Words',\n",
       " 'Crowdsourced Explanations for Humorous Internet Memes',\n",
       " 'LSDH: A Hashing Approach for Large-Scale Link Prediction in Microblogs',\n",
       " 'Identifying Domain-Dependent Influential Microblog Users: A Post-Feature Based Approach',\n",
       " 'RepRev: Mitigating the Negative Effects of Misreported Ratings',\n",
       " 'Reputation-Aware Continuous Double Auction',\n",
       " \"Computing Preferences Based on Agents' Beliefs\",\n",
       " 'Event Recommendation in Event-Based Social Networks',\n",
       " 'Coordination of Multiple Teams of Robots for an Optimal Global Plan',\n",
       " 'Inference Graphs: A New Kind of Hybrid Reasoning System',\n",
       " 'Online Multi-Task Gradient Temporal-Difference Learning',\n",
       " 'A Data Complexity Approach to Kernel Selection for Support Vector Machines',\n",
       " 'A Model Attention and Selection Framework for Estimation of Many Variables, with Applications to Estimating Object States in Large Spatial Environments',\n",
       " 'Converting Instance Checking to Subsumption: A Rethink for Object Queries over Practical Ontologies',\n",
       " 'Uncovering Hidden Structure through Parallel Problem Decomposition',\n",
       " 'Representing Words as Lymphocytes',\n",
       " 'Data Clustering by Laplacian Regularized L1-Graph',\n",
       " 'Fast Algorithm for Non-Stationary Gaussian Process Prediction',\n",
       " 'Inferring Causal Directions in Errors-in-Variables Models',\n",
       " 'Content-Structural Relation Inference in Knowledge Base',\n",
       " 'Efficient Top-k Shortest-Path Distance Queries on Large Networks by Pruned Landmark Labeling',\n",
       " 'Inferring Same-As Facts from Linked Data: An Iterative Import-by-Query Approach',\n",
       " 'A Personalized Interest-Forgetting Markov Model for Recommendations',\n",
       " 'Will You \"Reconsume\" the Near Past? Fast Prediction on Short-Term Reconsumption Behaviors',\n",
       " 'VELDA: Relating an Image Tweet’s Text and Images',\n",
       " 'On Information Coverage for Location Category Based Point-of-Interest Recommendation',\n",
       " 'Visually Interpreting Names as Demographic Attributes by Exploiting Click-Through Data',\n",
       " 'A New Granger Causal Model for Influence Evolution in Dynamic Social Networks: The Case of DBLP',\n",
       " 'An Axiomatic Approach to Link Prediction',\n",
       " 'Perceiving Group Themes from Collective Social and Behavioral Information',\n",
       " 'Predicting the Demographics of Twitter Users from Website Traffic Data',\n",
       " 'High-Performance Distributed ML at Scale through Parameter Server Consistency Models',\n",
       " 'An EBMC-Based Approach to Selecting Types for Entity Filtering',\n",
       " 'Trust Models for RDF Data: Semantics and Complexity',\n",
       " 'Extended Property Paths: Writing More SPARQL Queries in a Succinct Way',\n",
       " 'Lower and Upper Bounds for SPARQL Queries over OWL Ontologies',\n",
       " 'FACES: Diversity-Aware Entity Summarization Using Incremental Hierarchical Conceptual Clustering',\n",
       " 'TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings',\n",
       " 'A Stochastic Model for Detecting Heterogeneous Link Communities in Complex Networks',\n",
       " 'Prajna: Towards Recognizing Whatever You Want from Images without Image Labeling',\n",
       " 'Kernel Density Estimation for Text-Based Geolocation',\n",
       " 'Cross-Modal Image Clustering via Canonical Correlation Analysis',\n",
       " 'Modeling with Node Degree Preservation Can Accurately Find Communities',\n",
       " 'Estimating Temporal Dynamics of Human Emotions',\n",
       " 'Uniform Interpolation and Forgetting for ALC Ontologies with ABoxes',\n",
       " 'Using Matched Samples to Estimate the Effects of Exercise on Mental Health via Twitter',\n",
       " 'Consistent Knowledge Discovery from Evolving Ontologies',\n",
       " 'Multi-Document Summarization Based on Two-Level Sparse Representation Model',\n",
       " 'COT: Contextual Operating Tensor for Context-Aware Recommender Systems',\n",
       " 'Effectively Predicting Whether and When a Topic Will Become Prevalent in a Social Network',\n",
       " 'Content-Based Collaborative Filtering for News Topic Recommendation',\n",
       " 'A Tri-Role Topic Model for Domain-Specific Question Answering',\n",
       " 'Handling Owl:sameAs via Rewriting',\n",
       " 'Incorporating Assortativity and Degree Dependence into Scalable Network Models',\n",
       " 'Using Description Logics for RDF Constraint Checking and Closed-World Recognition',\n",
       " 'Approximating Model-Based ABox Revision in DL-Lite: Theory and Practice',\n",
       " 'Leveraging Social Foci for Information Seeking in Social Media',\n",
       " 'Extracting Bounded-Level Modules from Deductive RDF Triplestores',\n",
       " 'Question/Answer Matching for CQA System via Combining Lexical and Sequential Information',\n",
       " 'A Hybrid Approach of Classifier and Clustering for Solving the Missing Node Problem',\n",
       " 'Recommending Positive Links in Signed Social Networks by Optimizing a Generalized AUC',\n",
       " 'Causal Inference via Sparse Additive Models with Application to Online Advertising',\n",
       " 'Sampling Representative Users from Large Social Networks',\n",
       " 'Relating Romanized Comments to News Articles by Inferring Multi-Glyphic Topical Correspondence',\n",
       " 'Mining User Intents in Twitter: A Semi-Supervised Approach to Inferring Intent Categories for Tweets',\n",
       " 'Burst Time Prediction in Cascades',\n",
       " 'Clustering-Based Collaborative Filtering for Link Prediction',\n",
       " 'Mining Query Subtopics from Questions in Community Question Answering',\n",
       " 'DynaDiffuse: A Dynamic Diffusion Model for Continuous Time Constrained Influence Maximization',\n",
       " 'A Probabilistic Model for Bursty Topic Discovery in Microblogs',\n",
       " 'On the Scalable Learning of Stochastic Blockmodel',\n",
       " 'RAIN: Social Role-Aware Information Diffusion',\n",
       " 'Collaborative Topic Ranking: Leveraging Item Meta-Data for Sparsity Reduction',\n",
       " 'Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks',\n",
       " 'Are Features Equally Representative? A Feature-Centric Recommendation',\n",
       " 'Incorporating Implicit Link Preference Into Overlapping Community Detection',\n",
       " 'Retweet Behavior Prediction Using Hierarchical Dirichlet Process',\n",
       " 'Exploring Key Concept Paraphrasing Based on Pivot Language Translation for Question Retrieval',\n",
       " 'Representation Learning for Aspect Category Detection in Online Reviews',\n",
       " 'Person Identification Using Anthropometric and Gait Data from Kinect Sensor',\n",
       " 'R1SVM: A Randomised Nonlinear Approach to Large-Scale Anomaly Detection',\n",
       " 'Personalized Tag Recommendation through Nonlinear Tensor Factorization Using Gaussian Kernel',\n",
       " 'A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data',\n",
       " 'Probabilistic Graphical Models for Boosting Cardinal and Ordinal Peer Grading in MOOCs',\n",
       " 'Efficient Computation of Semivalues for Game-Theoretic Network Centrality',\n",
       " 'Embedded Unsupervised Feature Selection',\n",
       " 'Learning User-Specific Latent Influence and Susceptibility from Information Cascades',\n",
       " \"Kickback Cuts Backprop's Red-Tape: Biologically Plausible Credit Assignment in Neural Networks\",\n",
       " 'An Agent-Based Model of the Emergence and Transmission of a Language System for the Expression of Logical Combinations',\n",
       " 'Moral Decision-Making by Analogy: Generalizations versus Exemplars',\n",
       " 'AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis',\n",
       " 'Dialogue Understanding in a Logic of Action and Belief',\n",
       " 'Automated Construction of Visual-Linguistic Knowledge via Concept Learning from Cartoon Videos',\n",
       " 'Bayesian Affect Control Theory of Self',\n",
       " 'Heuristic Induction of Rate-Based Process Models',\n",
       " 'Spontaneous Retrieval from Long-Term Memory for a Cognitive Architecture',\n",
       " 'Learning Plausible Inferences from Semantic Web Knowledge by Combining Analogical Generalization with Structured Logistic Regression',\n",
       " 'Ontology-Based Information Extraction with a Cognitive Agent',\n",
       " 'Extending Analogical Generalization with Near-Misses',\n",
       " 'Automatic Ellipsis Resolution: Recovering Covert Information from Text',\n",
       " 'Inference Graphs: Combining Natural Deduction and Subsumption Inference in a Concurrent Reasoner',\n",
       " 'An Entorhinal-Hippocampal Model for Simultaneous Cognitive Map Building',\n",
       " 'An Association Network for Computing Semantic Relatedness',\n",
       " 'Influence-Driven Model for Time Series Prediction from Partial Observations',\n",
       " 'Sharing Rides with Friends: A Coalition Formation Algorithm for Ridesharing',\n",
       " 'Best-Response Planning of Thermostatically Controlled Loads under Power Constraints',\n",
       " 'FutureMatch: Combining Human Value Judgments and Machine Learning to Match in Dynamic Environments',\n",
       " 'Energy Disaggregation via Learning Powerlets and Sparse Coding',\n",
       " 'Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery',\n",
       " 'Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa',\n",
       " 'A Nonparametric Online Model for Air Quality Prediction',\n",
       " 'Power System Restoration With Transient Stability',\n",
       " 'Aggregating Electric Cars to Sustainable Virtual Power Plants: The Value of Flexibility in Future Electricity Markets',\n",
       " 'Energy Usage Behavior Modeling in Energy Disaggregation via Marked Hawkes Process',\n",
       " 'HVAC-Aware Occupancy Scheduling',\n",
       " 'Data Analysis and Optimization for (Citi)Bike Sharing',\n",
       " 'Towards Optimal Solar Tracking: A Dynamic Programming Approach',\n",
       " 'Risk Based Optimization for Improving Emergency Medical Systems',\n",
       " 'Predisaster Preparation of Transportation Networks',\n",
       " 'SmartShift: Expanded Load Shifting Incentive Mechanism for Risk-Averse Consumers',\n",
       " 'Incentivizing Users for Balancing Bike Sharing Systems',\n",
       " 'A Simulator of Human Emergency Mobility Following Disasters: Knowledge Transfer from Big Disaster Data',\n",
       " 'Real-Time Predictive Optimization for Energy Management in a Hybrid Electric Vehicle',\n",
       " 'Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games',\n",
       " 'Continuity Editing for 3D Animation',\n",
       " 'Assessing the Robustness of Cremer-McLean with Automated Mechanism Design',\n",
       " 'Online Learning and Profit Maximization from Revealed Preferences',\n",
       " 'Approximating Optimal Social Choice under Metric Preferences',\n",
       " 'Justified Representation in Approval-Based Committee Voting',\n",
       " 'Audit Games with Multiple Defender Resources',\n",
       " 'Learning Valuation Distributions from Partial Observation',\n",
       " 'Sequence-Form Algorithm for Computing Stackelberg Equilibria in Extensive-Form Games',\n",
       " 'Combining Compact Representation and Incremental Generation in Large Games with Sequential Strategies',\n",
       " 'Strategic Voting and Strategic Candidacy',\n",
       " 'A Faster Core Constraint Generation Algorithm for Combinatorial Auctions',\n",
       " 'Price Evolution in a Continuous Double Auction Prediction Market With a Scoring-Rule Based Market Maker',\n",
       " 'Computing Nash Equilibrium in Interdependent Defense Games',\n",
       " 'Fair Information Sharing for Treasure Hunting',\n",
       " 'Conventional Machine Learning for Social Choice',\n",
       " 'The Complexity of Recognizing Incomplete Single-Crossing Preferences',\n",
       " 'A Unifying Hierarchy of Valuations with Complements and Substitutes',\n",
       " 'Do Capacity Constraints Constrain Coalitions?',\n",
       " 'A Mechanism Design Approach to Measure Awareness',\n",
       " 'Facility Location with Double-Peaked Preferences',\n",
       " 'Elicitation for Aggregation',\n",
       " 'A Complexity Approach for Core-Selecting Exchange with Multiple Indivisible Goods under Lexicographic Preferences',\n",
       " 'Security Games with Protection Externalities',\n",
       " 'Strategy-Proof and Efficient Kidney Exchange Using a Credit Mechanism',\n",
       " 'Hedonic Coalition Formation in Networks',\n",
       " 'Matching with Dynamic Ordinal Preferences',\n",
       " 'On a Competitive Secretary Problem',\n",
       " 'Controlled School Choice with Soft Bounds and Overlapping Types',\n",
       " 'Optimal Personalized Filtering Against Spear-Phishing Attacks',\n",
       " 'Stable Invitations',\n",
       " 'The Pricing War Continues: On Competitive Multi-Item Pricing',\n",
       " 'Cooperative Game Solution Concepts that Maximize Stability under Noise',\n",
       " 'Congestion Games with Distance-Based Strict Uncertainty',\n",
       " 'On the Convergence of Iterative Voting: How Restrictive Should Restricted Dynamics Be?',\n",
       " 'Voting Rules As Error-Correcting Codes',\n",
       " 'Analysis of Equilibria in Iterative Voting Schemes',\n",
       " 'Incentives for Subjective Evaluations with Private Beliefs',\n",
       " 'Envy-Free Cake-Cutting in Two Dimensions',\n",
       " 'Truthful Mechanisms without Money for Non-Utilitarian Heterogeneous Facility Location',\n",
       " 'A Graphical Representation for Games in Partition Function Form',\n",
       " 'A Stackelberg Game Approach for Incentivizing Participation in Online Educational Forums with Heterogeneous Student Population',\n",
       " 'Mechanism Design for Team Formation',\n",
       " 'Exploring Information Asymmetry in Two-Stage Security Games',\n",
       " 'Balanced Trade Reduction for Dual-Role Exchange Markets',\n",
       " 'Optimal Machine Strategies to Commit to in Two-Person Repeated Games',\n",
       " 'Optimal Column Subset Selection by A-Star Search',\n",
       " 'Limitations of Front-To-End Bidirectional Heuristic Search',\n",
       " 'Incremental Weight Elicitation for Multiobjective State Space Search',\n",
       " 'Complexity Results for Compressing Optimal Paths',\n",
       " 'Two Weighting Local Search for Minimum Vertex Cover',\n",
       " 'Efficient Benchmarking of Hyperparameter Optimizers via Surrogates',\n",
       " 'Convergent Plans for Large-Scale Evacuations',\n",
       " 'Initializing Bayesian Hyperparameter Optimization via Meta-Learning',\n",
       " 'Stochastic Local Search for Satisfiability Modulo Theories',\n",
       " 'Lagrangian Decomposition Algorithm for Allocating Marketing Channels',\n",
       " 'Recursive Best-First Search with Bounded Overhead',\n",
       " 'Reusing Previously Found A* Paths for Fast Goal-Directed Navigation in Dynamic Terrain',\n",
       " 'Pruning Game Tree by Rollouts',\n",
       " 'Solving Distributed Constraint Optimization Problems Using Logic Programming',\n",
       " 'Value-Directed Compression of Large-Scale Assignment Problems',\n",
       " 'On Unconstrained Quasi-Submodular Function Optimization',\n",
       " 'Improved Local Search for Binary Matrix Factorization',\n",
       " 'A Theoretical Analysis of Optimization by Gaussian Continuation',\n",
       " 'Solving Hard Stable Matching Problems via Local Search and Cooperative Parallelization',\n",
       " 'BDD-Constrained Search: A Unified Approach to Constrained Shortest Path Problems',\n",
       " 'Exploiting Variable Associations to Configure Efficient Local Search in Large-Scale Set Partitioning Problems',\n",
       " 'Resilient Upgrade of Electrical Distribution Grids',\n",
       " 'TDS+: Improving Temperature Discovery Search',\n",
       " 'Massively Parallel A* Search on a GPU',\n",
       " 'Novel Mechanisms for Online Crowdsourcing with Unreliable, Strategic Agents',\n",
       " 'Acquiring Speech Transcriptions Using Mismatched Crowdsourcing',\n",
       " 'Incentive Networks',\n",
       " 'Collaboration in Social Problem-Solving: When Diversity Trumps Network Efficiency',\n",
       " 'CrowdWON: A Modelling Language for Crowd Processes based on Workflow Nets',\n",
       " 'On the Impossibility of Convex Inference in Human Computation',\n",
       " 'Crowdsourcing Complex Workflows under Budget Constraints',\n",
       " 'Efficient Task Sub-Delegation for Crowdsourcing',\n",
       " 'When Suboptimal Rules',\n",
       " 'Providing Arguments in Discussions Based on the Prediction of Human Argumentative Behavior',\n",
       " 'Predicting Emotion Perception Across Domains: A Study of Singing and Speaking',\n",
       " 'Learning to Manipulate Unknown Objects in Clutter by Reinforcement',\n",
       " \"Bayesian Active Learning-Based Robot Tutor for Children's Word-Reading Skills\",\n",
       " 'RANSAC versus CS-RANSAC',\n",
       " 'Game-Theoretic Approach for Non-Cooperative Planning',\n",
       " 'Pattern-Based Variant-Best-Neighbors Respiratory Motion Prediction Using Orthogonal Polynomials Approximation',\n",
       " 'Toward Mobile Robots Reasoning Like Humans',\n",
       " 'Integration and Evaluation of a Matrix Factorization Sequencer in Large Commercial ITS',\n",
       " 'Going Beyond Literal Command-Based Instructions: Extending Robotic Natural Language Interaction Capabilities',\n",
       " 'CORPP: Commonsense Reasoning and Probabilistic Planning, as Applied to Dialog with a Mobile Robot',\n",
       " 'Tackling Mental Health by Integrating Unobtrusive Multimodal Sensing',\n",
       " 'Ontology Module Extraction via Datalog Reasoning',\n",
       " 'Tractable Interval Temporal Propositional and Description Logics',\n",
       " 'Action Language BC+: Preliminary Report',\n",
       " 'LARS: A Logic-Based Framework for Analyzing Reasoning over Streams',\n",
       " 'Partial Meet Revision and Contraction in Logic Programs',\n",
       " \"Pearl's Causality in a Logical Setting\",\n",
       " 'Grounded Fixpoints',\n",
       " 'Solving and Explaining Analogy Questions Using Semantic Networks',\n",
       " 'asprin: Customizing Answer Set Preferences without a Headache',\n",
       " 'Exploiting Parallelism for Hard Problems in Abstract Argumentation',\n",
       " 'A Syntax-Independent Approach to Forgetting in Disjunctive Logic Programs',\n",
       " 'Towards Tractable and Practical ABox Abduction over Inconsistent Description Logic Ontologies',\n",
       " 'On Computing Explanations in Argumentation',\n",
       " 'Parallelized Hitting Set Computation for Model-Based Diagnosis',\n",
       " 'Splitting a Logic Program Revisited',\n",
       " 'On Elementary Loops and Proper Loops for Disjunctive Logic Programs',\n",
       " 'XPath for DL Ontologies',\n",
       " 'An Abstract View on Modularity in Knowledge Representation',\n",
       " 'Learning Partial Lexicographic Preference Trees over Combinatorial Domains',\n",
       " 'From Classical to Consistent Query Answering under Existential Rules',\n",
       " 'Belief Revision with General Epistemic States',\n",
       " 'Incremental Update of Datalog Materialisation: the Backward/Forward Algorithm',\n",
       " 'Logic Programming in Assumption-Based Argumentation Revisited - Semantics and Graphical Representation',\n",
       " 'Minimizing User Involvement for Accurate Ontology Matching Problems',\n",
       " 'Projection in the Epistemic Situation Calculus with Belief Conditionals',\n",
       " 'Belief Revision Games',\n",
       " 'Interactive Query-Based Debugging of ASP Programs',\n",
       " 'Exploring the KD45 Property of a Kripke Model After the Execution of an Action Sequence',\n",
       " 'Answering Conjunctive Queries over EL Knowledge Bases with Transitive and Reflexive Roles',\n",
       " 'How Many Diagnoses Do We Need?',\n",
       " 'The Relative Expressiveness of Abstract Argumentation and Logic Programming',\n",
       " 'A Comparison of Qualitative and Metric Spatial Relation Models for Scene Understanding',\n",
       " 'On the Role of Canonicity in Knowledge Compilation',\n",
       " 'Knowledge Forgetting in Circumscription: A Preliminary Report',\n",
       " 'Instance-Driven Ontology Evolution in DL-Lite',\n",
       " 'Structured Embedding via Pairwise Relations and Long-Range Interactions in Knowledge Base',\n",
       " 'A Logic for Reasoning About Game Strategies',\n",
       " 'Existential Rule Languages with Finite Chase: Complexity and Expressiveness',\n",
       " 'Variational Inference for Nonparametric Bayesian Quantile Regression',\n",
       " 'Sample-Targeted Clinical Trial Adaptation',\n",
       " 'A Sparse Combined Regression-Classification Formulation for Learning a Physiological Alternative to Clinical Post-Traumatic Stress Disorder Scores',\n",
       " 'Marginalized Denoising for Link Prediction and Multi-Label Learning',\n",
       " 'Structured Sparsity with Group-Graph Regularization',\n",
       " 'Content-Aware Point of Interest Recommendation on Location-Based Social Networks',\n",
       " 'Constructing Models of User and Task Characteristics from Eye Gaze Data for User-Adaptive Information Highlighting',\n",
       " 'Automatic Assessment of OCR Quality in Historical Documents',\n",
       " 'PD Disease State Assessment in Naturalistic Environments Using Deep Learning',\n",
       " 'Identifying At-Risk Students in Massive Open Online Courses',\n",
       " 'Exploiting Determinism to Scale Relational Inference',\n",
       " 'Scalable and Interpretable Data Representation for High-Dimensional, Complex Data',\n",
       " 'Learning to Uncover Deep Musical Structure',\n",
       " 'Nonstationary Gaussian Process Regression for Evaluating Repeated Clinical Laboratory Tests',\n",
       " 'Tensor-Based Learning for Predicting Stock Movements',\n",
       " 'Sub-Merge: Diving Down to the Attribute-Value Level in Statistical Schema Matching',\n",
       " 'A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis',\n",
       " 'Generalized Singular Value Thresholding',\n",
       " 'Lazier Than Lazy Greedy',\n",
       " 'Inertial Hidden Markov Models: Modeling Change in Multivariate Time Series',\n",
       " 'Algorithm Selection via Ranking',\n",
       " 'Propagating Ranking Functions on a Graph: Algorithms and Applications',\n",
       " 'On Vectorization of Deep Convolutional Neural Networks for Vision Tasks',\n",
       " 'Learning Hybrid Models with Guarded Transitions',\n",
       " 'Transaction Costs-Aware Portfolio Optimization via Fast Lowner-John Ellipsoid Approximation',\n",
       " 'Coupled Interdependent Attribute Analysis on Mixed Data',\n",
       " 'Exploring Social Context for Topic Identification in Short and Noisy Texts',\n",
       " 'Modeling Status Theory in Trust Prediction',\n",
       " 'Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification',\n",
       " 'An SVD and Derivative Kernel Approach to Learning from Geometric Data',\n",
       " 'Mining User Interests from Personal Photos',\n",
       " 'Integrating Image Clustering and Codebook Learning',\n",
       " 'Stable Feature Selection from Brain sMRI',\n",
       " 'Forecasting Collector Road Speeds Under High Percentage of Missing Data',\n",
       " 'Large-Margin Multi-Label Causal Feature Learning',\n",
       " 'Exploiting Task-Feature Co-Clusters in Multi-Task Learning',\n",
       " 'Temporally Adaptive Restricted Boltzmann Machine for Background Modeling',\n",
       " 'On Machine Learning towards Predictive Sales Pipeline Analytics',\n",
       " 'Bayesian Approach to Modeling and Detecting Communities in Signed Network',\n",
       " 'Colorization by Patch-Based Local Low-Rank Matrix Completion',\n",
       " \"Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimer's Disease\",\n",
       " 'A Closed Form Solution to Multi-View Low-Rank Regression',\n",
       " 'A Nonconvex Relaxation Approach for Rank Minimization Problems',\n",
       " 'An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types',\n",
       " 'Scalable Planning and Learning for Multiagent POMDPs',\n",
       " 'Multi-Agent Pathfinding as a Combinatorial Auction',\n",
       " 'Cooperating with Unknown Teammates in Complex Domains: A Robot Soccer Case Study of Ad Hoc Teamwork',\n",
       " 'Cognitive Social Learners: An Architecture for Modeling Normative Behavior',\n",
       " 'Multi-Agent Path Finding on Strongly Biconnected Digraphs',\n",
       " 'Verification of Relational Multiagent Systems with Data Types',\n",
       " 'Verifying and Synthesising Multi-Agent Systems against One-Goal Strategy Logic Specifications',\n",
       " 'Elections with Few Voters: Candidate Control Can Be Easy',\n",
       " 'Cupid: Commitments in Relational Algebra',\n",
       " 'Automated Analysis of Commitment Protocols Using Probabilistic Model Checking',\n",
       " 'Fast Convention Formation in Dynamic Networks Using Topological Knowledge',\n",
       " 'Distributed Multiplicative Weights Methods for DCOP',\n",
       " 'A Counter Abstraction Technique for the Verification of Robot Swarms',\n",
       " 'Generalization Analysis for Game-Theoretic Machine Learning',\n",
       " 'SCRAM: Scalable Collision-avoiding Role Assignment with Minimal-Makespan for Formational Positioning',\n",
       " 'Plurality Voting Under Uncertainty',\n",
       " 'Multi-Robot Auctions for Allocation of Tasks with Temporal Constraints',\n",
       " 'Distributing Coalition Value Calculations to Coalition Members',\n",
       " 'Fully Proportional Representation with Approval Ballots: Approximating the MaxCover Problem with Bounded Frequencies in FPT Time',\n",
       " 'Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation',\n",
       " 'Solving Games with Functional Regret Estimation',\n",
       " 'Learning Word Representations from Relational Graphs',\n",
       " 'Ranking with Recursive Neural Networks and Its Application to Multi-Document Summarization',\n",
       " 'Refer-to-as Relations as Semantic Knowledge',\n",
       " 'Surveyor: A System for Generating Coherent Survey Articles for Scientific Topics',\n",
       " 'Automatically Creating a Large Number of New Bilingual Dictionaries',\n",
       " 'Learning Entity and Relation Embeddings for Knowledge Graph Completion',\n",
       " 'Sense-Aaware Semantic Analysis: A Multi-Prototype Word Representation Model Using Wikipedia',\n",
       " 'Phrase Type Sensitive Tensor Indexing Model for Semantic Composition',\n",
       " 'Predicting Peer-to-Peer Loan Rates Using Bayesian Non-Linear Regression',\n",
       " 'A Novel Neural Topic Model and Its Supervised Extension',\n",
       " 'Unsupervised Word Sense Disambiguation Using Markov Random Field and Dependency Parser',\n",
       " 'Dataless Text Classification with Descriptive LDA',\n",
       " 'Topic Segmentation with an Ordering-Based Topic Model',\n",
       " 'A Stratified Strategy for Efficient Kernel-Based Learning',\n",
       " 'Weakly-Supervised Grammar-Informed Bayesian CCG Parser Learning',\n",
       " 'Unsupervised Phrasal Near-Synonym Generation from Text Corpora',\n",
       " 'Local Context Sparse Coding',\n",
       " 'Recurrent Convolutional Neural Networks for Text Classification',\n",
       " 'Joint Anaphoricity Detection and Coreference Resolution with Constrained Latent Structures',\n",
       " 'Fast and Accurate Prediction of Sentence Specificity',\n",
       " 'Learning to Mediate Perceptual Differences in Situated Human-Robot Dialogue',\n",
       " 'Contrastive Unsupervised Word Alignment with Non-Local Features',\n",
       " 'Never-Ending Learning',\n",
       " 'The Utility of Text: The Case of Amicus Briefs and the Supreme Court',\n",
       " 'A Family of Latent Variable Convex Relaxations for IBM Model 2',\n",
       " 'Online Bayesian Models for Personal Analytics in Social Media',\n",
       " 'Microblog Sentiment Classification with Contextual Knowledge Regularization',\n",
       " 'Learning Greedy Policies for the Easy-First Framework',\n",
       " 'Jointly Modeling Deep Video and Compositional Text to Bridge Vision and Language in a Unified Framework',\n",
       " 'Ordering-Sensitive and Semantic-Aware Topic Modeling',\n",
       " 'Target-Dependent Churn Classification in Microblogs',\n",
       " 'English Light Verb Construction Identification Using Lexical Knowledge',\n",
       " 'Chinese Common Noun Phrase Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers',\n",
       " 'Gazetteer-Independent Toponym Resolution Using Geographic Word Profiles',\n",
       " 'Mining User Consumption Intention from Social Media Using Domain Adaptive Convolutional Neural Network',\n",
       " 'Generating Event Causality Hypotheses through Semantic Relations',\n",
       " 'A Neural Probabilistic Model for Context Based Citation Recommendation',\n",
       " 'Extracting Verb Expressions Implying Negative Opinions',\n",
       " 'Topical Word Embeddings',\n",
       " 'Towards Knowledge-Driven Annotation',\n",
       " 'Semantic Lexicon Induction from Twitter with Pattern Relatedness and Flexible Term Length',\n",
       " 'Word Segmentation for Chinese Novels',\n",
       " 'Using Frame Semantics for Knowledge Extraction from Twitter',\n",
       " 'Learning to Recommend Quotes for Writing',\n",
       " 'Extracting Adverse Drug Reactions from Social Media',\n",
       " 'An Unsupervised Framework of Exploring Events on Twitter: Filtering, Extraction and Categorization',\n",
       " 'A Probabilistic Covariate Shift Assumption for Domain Adaptation',\n",
       " 'Efficient Active Learning of Halfspaces via Query Synthesis',\n",
       " 'Budgeted Prediction with Expert Advice',\n",
       " 'Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization',\n",
       " 'Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment',\n",
       " 'Aligning Mixed Manifolds',\n",
       " 'Deep Modeling Complex Couplings within Financial Markets',\n",
       " 'Structural Learning with Amortized Inference',\n",
       " 'A Convex Formulation for Spectral Shrunk Clustering',\n",
       " 'Learning Relational Kalman Filtering',\n",
       " 'Policy Tree: Adaptive Representation for Policy Gradient',\n",
       " 'Collaborative Filtering with Localised Ranking',\n",
       " 'Random Gradient Descent Tree: A Combinatorial Approach for SVM with Outliers',\n",
       " 'An Adaptive Gradient Method for Online AUC Maximization',\n",
       " 'Graph-Sparse LDA: A Topic Model with Structured Sparsity',\n",
       " 'Bayesian Maximum Margin Principal Component Analysis',\n",
       " 'Modelling Class Noise with Symmetric and Asymmetric Distributions',\n",
       " 'Optimizing Bag Features for Multiple-Instance Retrieval',\n",
       " 'Learning Sparse Representations from Datasets with Uncertain Group Structures: Model, Algorithm and Applications',\n",
       " 'Spectral Clustering Using Multilinear SVD: Analysis, Approximations and Applications',\n",
       " 'Pathway Graphical Lasso',\n",
       " 'Concurrent PAC RL',\n",
       " 'Discriminative Feature Grouping',\n",
       " 'Learning Multi-Level Task Groups in Multi-Task Learning',\n",
       " 'Localized Centering: Reducing Hubness in Large-Sample Data',\n",
       " 'Expressing Arbitrary Reward Functions as Potential-Based Advice',\n",
       " 'Active Learning by Learning',\n",
       " 'Kernelized Online Imbalanced Learning with Fixed Budgets',\n",
       " 'Approximate MaxEnt Inverse Optimal Control and Its Application for Mental Simulation of Human Interactions',\n",
       " 'Maximin Separation Probability Clustering',\n",
       " 'The Dynamic Chinese Restaurant Process via Birth and Death Processes',\n",
       " 'Self-Paced Curriculum Learning',\n",
       " 'Outlier-Robust Convex Segmentation',\n",
       " 'Fast Gradient Descent for Drifting Least Squares Regression, with Application to Bandits',\n",
       " 'Spectral Learning of Predictive State Representations with Insufficient Statistics',\n",
       " 'A Generalized Reduced Linear Program for Markov Decision Processes',\n",
       " \"Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX\",\n",
       " 'On the Equivalence of Linear Discriminant Analysis and Least Squares',\n",
       " 'Multi-tensor Completion with Common Structures',\n",
       " 'Large-Scale Multi-View Spectral Clustering via Bipartite Graph',\n",
       " 'Integrating Features and Similarities: Flexible Models for Heterogeneous Multiview Data',\n",
       " 'Shift-Pessimistic Active Learning Using Robust Bias-Aware Prediction',\n",
       " 'Unidimensional Clustering of Discrete Data Using Latent Tree Models',\n",
       " 'Low-Rank Multi-View Learning in Matrix Completion for Multi-Label Image Classification',\n",
       " 'Support Consistency of Direct Sparse-Change Learning in Markov Networks',\n",
       " 'Low-Rank Similarity Metric Learning in High Dimensions',\n",
       " 'Large Margin Metric Learning for Multi-Label Prediction',\n",
       " 'Absent Multiple Kernel Learning',\n",
       " 'Eigenvalues Ratio for Kernel Selection of Kernel Methods',\n",
       " 'Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation',\n",
       " 'Noise-Robust Semi-Supervised Learning by Large-Scale Sparse Coding',\n",
       " 'The Hybrid Nested/Hierarchical Dirichlet Process and its Application to Topic Modeling with Word Differentiation',\n",
       " 'UT Austin Villa 2014: RoboCup 3D Simulation League Champion via Overlapping Layered Learning',\n",
       " 'The Queue Method: Handling Delay, Heuristics, Prior Data, and Evaluation in Bandits',\n",
       " 'V-MIN: Efficient Reinforcement Learning through Demonstrations and Relaxed Reward Demands',\n",
       " 'The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning',\n",
       " 'Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners',\n",
       " 'Learning Relational Sum-Product Networks',\n",
       " 'Tensor-Variate Restricted Boltzmann Machines',\n",
       " 'Probabilistic Attributed Hashing',\n",
       " 'Obtaining Well Calibrated Probabilities Using Bayesian Binning',\n",
       " 'Detecting and Tracking Concept Class Drift and Emergence in Non-Stationary Fast Data Streams',\n",
       " 'Detecting Change Points in the Large-Scale Structure of Evolving Networks',\n",
       " 'Adaptive Sampling with Optimal Cost for Class-Imbalance Learning',\n",
       " 'Multi-Objective Reinforcement Learning with Continuous Pareto Frontier Approximation',\n",
       " 'Pareto Ensemble Pruning',\n",
       " 'Leveraging Features and Networks for Probabilistic Tensor Decomposition',\n",
       " 'Doubly Robust Covariate Shift Correction',\n",
       " 'Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery',\n",
       " 'SP-SVM: Large Margin Classifier for Data on Multiple Manifolds',\n",
       " 'Spectral Label Refinement for Noisy and Missing Text Labels',\n",
       " 'Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation',\n",
       " 'Agnostic System Identification for Monte Carlo Planning',\n",
       " 'Optimizing the CVaR via Sampling',\n",
       " 'High-Confidence Off-Policy Evaluation',\n",
       " 'TODTLER: Two-Order-Deep Transfer Learning',\n",
       " 'Compress and Control',\n",
       " 'Improving Multi-Step Prediction of Learned Time Series Models',\n",
       " 'Gaussian Cardinality Restricted Boltzmann Machines',\n",
       " 'Online Boosting Algorithms for Anytime Transfer and Multitask Learning',\n",
       " 'Convex Batch Mode Active Sampling via α-Relative Pearson Divergence',\n",
       " 'Relational Stacked Denoising Autoencoder for Tag Recommendation',\n",
       " 'Learning Robust Locality Preserving Projection via p-Order Minimization',\n",
       " 'Learning to Hash on Structured Data',\n",
       " 'Transfer Feature Representation via Multiple Kernel Learning',\n",
       " 'Optimal Estimation of Multivariate ARMA Models',\n",
       " 'Improving Approximate Value Iteration with Complex Returns by Bounding',\n",
       " 'Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time',\n",
       " 'Dictionary Learning with Mutually Reinforcing Group-Graph Structures',\n",
       " 'Active Manifold Learning via Gershgorin Circle Guided Sample Selection',\n",
       " 'Nystrom Approximation for Sparse Kernel Methods: Theoretical Analysis and Empirical Evaluation',\n",
       " 'OMNI-Prop: Seamless Node Classification on Arbitrary Label Correlation',\n",
       " 'Non-Linear Regression for Bag-of-Words Data via Gaussian Process Latent Variable Set Model',\n",
       " 'A Mathematical Programming-Based Approach to Determining Objective Functions from Qualitative and Subjective Comparisons',\n",
       " 'Exact Recoverability of Robust PCA via Outlier Pursuit with Tight Recovery Bounds',\n",
       " 'Multi-Source Domain Adaptation: A Causal View',\n",
       " 'Online Bandit Learning for a Special Class of Non-Convex Losses',\n",
       " 'Online Dictionary Learning on Symmetric Positive Definite Manifolds with Vision Applications',\n",
       " 'Constrained NMF-Based Multi-View Clustering on Unmapped Data',\n",
       " 'Multi-Task Learning and Algorithmic Stability',\n",
       " 'SoF: Soft-Cluster Matrix Factorization for Probabilistic Clustering',\n",
       " 'Self-Paced Learning for Matrix Factorization',\n",
       " 'Cross-Modal Similarity Learning via Pairs, Preferences, and Active Supervision',\n",
       " 'A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing',\n",
       " '10, 000+ Times Accelerated Robust Subset Selection',\n",
       " 'Tractable Cost-Optimal Planning over Restricted Polytree Causal Graphs',\n",
       " 'Some Fixed Parameter Tractability Results for Planning with Non-Acyclic Domain-Transition Graphs',\n",
       " 'Robustness in Probabilistic Temporal Planning',\n",
       " 'SMT-Based Nonlinear PDDL+ Planning',\n",
       " 'Strong Temporal Planning with Uncontrollable Durations: A State-Space Approach',\n",
       " 'Factored MCTS for Large Scale Stochastic Planning',\n",
       " 'Transition Constraints for Parallel Planning',\n",
       " 'Measuring Plan Diversity: Pathologies in Existing Approaches and A New Plan Distance Metric',\n",
       " 'Efficient Bounds in Heuristic Search Algorithms for Stochastic Shortest Path Problems',\n",
       " 'A Generalization of Sleep Sets Based on Operator Sequence Redundancy',\n",
       " 'Goal Recognition Design for Non-Optimal Agents',\n",
       " 'Variable-Deletion Backdoors to Planning',\n",
       " 'Preference Planning for Markov Decision Processes',\n",
       " 'Information Gathering and Reward Exploitation of Subgoals for POMDPs',\n",
       " 'Planning Over Multi-Agent Epistemic States: A Classical Planning Approach',\n",
       " 'From Non-Negative to General Operator Cost Partitioning',\n",
       " 'Approximate Linear Programming for Constrained Partially Observable Markov Decision Processes',\n",
       " 'Discretization of Temporal Models with Application to Planning with SMT',\n",
       " 'Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection',\n",
       " 'Automatic Configuration of Sequential Planning Portfolios',\n",
       " 'Heuristics and Symmetries in Classical Planning',\n",
       " 'Factored Symmetries for Merge-and-Shrink Abstractions',\n",
       " 'Improving Exploration in UCT Using Local Manifolds',\n",
       " 'Tractability of Planning with Loops',\n",
       " 'Real-Time Symbolic Dynamic Programming',\n",
       " 'tBurton: A Divide and Conquer Temporal Planner',\n",
       " 'Multi-Objective MDPs with Conditional Lexicographic Reward Preferences',\n",
       " 'Resolving Over-Constrained Probabilistic Temporal Problems through Chance Constraint Relaxation',\n",
       " 'An Efficient Forest-Based Tabu Search Algorithm for the Split-delivery Vehicle Routing Problem',\n",
       " 'Crowdsourced Action-Model Acquisition for Planning',\n",
       " 'Loss-Calibrated Monte Carlo Action Selection',\n",
       " 'Solving Uncertain MDPs with Objectives that Are Separable over Instantiations of Model Uncertainty',\n",
       " 'Linear-Time Gibbs Sampling in Piecewise Graphical Models',\n",
       " 'Stable Model Counting and Its Application in Probabilistic Logic Programming',\n",
       " 'Recovering Causal Effects from Selection Bias',\n",
       " 'Egalitarian Collective Decision Making under Qualitative Possibilistic Uncertainty: Principles and Characterization',\n",
       " 'Representing Aggregators in Relational Probabilistic Models',\n",
       " 'Optimal Cost Almost-Sure Reachability in POMDPs',\n",
       " 'Value of Information Based on Decision Robustness',\n",
       " 'Submodular Surrogates for Value of Information',\n",
       " 'Bayesian Networks Specified Using Propositional and Relational Constructs: Combined, Data, and Domain Complexity',\n",
       " 'An Improved Lower Bound for Bayesian Network Structure Learning',\n",
       " 'Approximately Optimal Risk-Averse Routing Policies via Adaptive Discretization',\n",
       " 'Better Be Lucky than Good: Exceeding Expectations in MDP Evaluation',\n",
       " 'Reward Shaping for Model-Based Bayesian Reinforcement Learning',\n",
       " 'Tighter Value Function Bounds for Bayesian Reinforcement Learning',\n",
       " 'Knowledge-Based Probabilistic Logic Learning',\n",
       " 'On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions',\n",
       " 'Representation Discovery for MDPs Using Bisimulation Metrics',\n",
       " 'Lifting Model Sampling for General Game Playing to Incomplete-Information Models',\n",
       " 'On Interruptible Pure Exploration in Multi-Armed Bandits',\n",
       " 'Lifted Probabilistic Inference for Asymmetric Graphical Models',\n",
       " 'Just Count the Satisfied Groundings: Scalable Local-Search and Sampling Based Inference in MLNs',\n",
       " 'Hierarchical Monte-Carlo Planning',\n",
       " 'Chance-Constrained Scheduling via Conflict-Directed Risk Allocation',\n",
       " 'Learning to Reject Sequential Importance Steps for Continuous-Time Bayesian Networks',\n",
       " 'Nonparametric Scoring Rules',\n",
       " 'On Fairness in Decision-Making under Uncertainty: Definitions, Computation, and Comparison',\n",
       " 'An Exact Algorithm for Solving Most Relevant Explanation in Bayesian Networks',\n",
       " 'Proximal Operators for Multi-Agent Path Planning',\n",
       " 'This Time the Robot Settles for a Cost: A Quantitative Approach to Temporal Logic Planning with Partial Satisfaction',\n",
       " 'Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation',\n",
       " 'Spatio-Spectral Exploration Combining In Situ and Remote Measurements',\n",
       " 'Robot Learning Manipulation Action Plans by \"Watching\" Unconstrained Videos from the World Wide Web',\n",
       " 'Efficient Extraction of QBF (Counter)models from Long-Distance Resolution Proofs',\n",
       " 'SAT Modulo Monotonic Theories',\n",
       " 'On Computing Maximal Subsets of Clauses that Must Be Satisfiable with Possibly Mutually-Contradictory Assumptive Contexts',\n",
       " 'Strong Bounds Consistencies and Their Application to Linear Constraints',\n",
       " 'SMT-Based Validation of Timed Failure Propagation Graphs',\n",
       " 'Binarisation via Dualisation for Valued Constraints',\n",
       " 'SAT-Based Strategy Extraction in Reachability Games',\n",
       " 'The Extendable-Triple Property: A New CSP Tractable Class beyond BTP',\n",
       " 'Online Detection of Abnormal Events Using Incremental Coding Length',\n",
       " 'A Bayesian Approach to Perceptual 3D Object-Part Decomposition Using Skeleton-Based Representations',\n",
       " 'Exploring Semantic Inter-Class Relationships (SIR) for Zero-Shot Action Recognition',\n",
       " 'Building Effective Representations for Sketch Recognition',\n",
       " 'Learning Predictable and Discriminative Attributes for Visual Recognition',\n",
       " 'A Local Sparse Model for Matching Problem',\n",
       " ...]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "with open('AAAI_pdfs_all.json','r') as f:\n",
    "    rrr2 = json.load(f)\n",
    "res22 = [j for i in rrr2 for j in rrr2[i]]\n",
    "res22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3502"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(res11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5064"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(res22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ['2']\n",
    "import json\n",
    "with open('123.txt','w') as f:\n",
    "    json.dump(x,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pdfminer.layout import LAParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mInit signature:\u001b[0m\n",
       "\u001b[0mLAParams\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mline_overlap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mchar_margin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mline_margin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mword_margin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mboxes_flow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mdetect_vertical\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mall_texts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m     \n",
       "Parameters for layout analysis\n",
       "\n",
       ":param line_overlap: If two characters have more overlap than this they\n",
       "    are considered to be on the same line. The overlap is specified\n",
       "    relative to the minimum height of both characters.\n",
       ":param char_margin: If two characters are closer together than this\n",
       "    margin they are considered to be part of the same word. If\n",
       "    characters are on the same line but not part of the same word, an\n",
       "    intermediate space is inserted. The margin is specified relative to\n",
       "    the width of the character.\n",
       ":param word_margin: If two words are are closer together than this\n",
       "    margin they are considered to be part of the same line. A space is\n",
       "    added in between for readability. The margin is specified relative\n",
       "    to the width of the word.\n",
       ":param line_margin: If two lines are are close together they are\n",
       "    considered to be part of the same paragraph. The margin is\n",
       "    specified relative to the height of a line.\n",
       ":param boxes_flow: Specifies how much a horizontal and vertical position\n",
       "    of a text matters when determining the order of text boxes. The value\n",
       "    should be within the range of -1.0 (only horizontal position\n",
       "    matters) to +1.0 (only vertical position matters).\n",
       ":param detect_vertical: If vertical text should be considered during\n",
       "    layout analysis\n",
       ":param all_texts: If layout analysis should be performed on text in\n",
       "    figures.\n",
       "\u001b[1;31mFile:\u001b[0m           d:\\anaconda3\\lib\\site-packages\\pdfminer\\layout.py\n",
       "\u001b[1;31mType:\u001b[0m           type\n",
       "\u001b[1;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "laparams=LAParams(\n",
    "    line_overlap=0.5,\n",
    "    char_margin=2.0,\n",
    "    line_margin=0.5,\n",
    "    word_margin=0.1,\n",
    "    boxes_flow=0.5,\n",
    "    detect_vertical=True,\n",
    "    all_texts=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "laparams=LAParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mysql.connector\n",
    "\n",
    "cnx = mysql.connector.connect(user='xiong', password='xiong',\n",
    "                              host='127.0.0.1',\n",
    "                              database='xionggm_db')\n",
    "cnx.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mysql.connector.connection_cext.CMySQLConnection at 0x7fe7afd87390>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database version : 8.0.19 \n"
     ]
    }
   ],
   "source": [
    "import pymysql\n",
    "\n",
    "db = pymysql.connect(\"localhost\",\"xiong\",\"xiong\",\"xionggm_db\" )\n",
    "\n",
    "cursor = db.cursor()\n",
    " \n",
    "cursor.execute(\"SELECT VERSION()\")\n",
    " \n",
    "data = cursor.fetchone()\n",
    " \n",
    "print (\"Database version : %s \" % data)\n",
    " \n",
    "db.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from selenium import webdriver\n",
    "import time\n",
    "\n",
    "options = webdriver.ChromeOptions() \n",
    "options.add_argument('--headless') \n",
    "options.add_argument('--disable-gpu') \n",
    "options.add_argument(\"--no-sandbox\") \n",
    "driver = webdriver.Chrome(options=options,executable_path='./chromedriver')\n",
    "\n",
    "driver.get(\"https://www.aaai.org/ojs/index.php/AAAI/article/view/5010/4883\")\n",
    "driver.maximize_window()\n",
    "\n",
    "driver.get_screenshot_as_file('./baidu.png')\n",
    "driver.save_screenshot('./baidu2.png')\n",
    "\n",
    "driver.quit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The Thirty-First AAAI Conference on Innovative Applications of Artiﬁcial Intelligence (IAAI-19)\\n\\nEnsemble Machine Learning for\\n\\nEstimating Fetal Weight at Varying Gestational Age\\n\\nYu Lu,1 Xi Zhang,1 Xianghua Fu,1 Fangxiong Chen,2 Kelvin K. L. Wong3,∗\\n\\n1Faculty of Arts and Sciences, Shenzhen Technology University, Shenzhen, China\\n2School of Automation, Guangdong University of Technology, Guangzhou, China\\n\\n3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China\\n\\n∗Corresponding author: kelvin.wong@siat.ac.cn\\n\\nAbstract\\n\\nObstetric ultrasound examination of physiological parame-\\nters has been mainly used to estimate the fetal weight dur-\\ning pregnancy and baby weight before labour to monitor fetal\\ngrowth and reduce prenatal morbidity and mortality. How-\\never, the problem is that ultrasound estimation of fetal weight\\nis subject to populations’ difference, strict operating require-\\nments for sonographers, and poor access to ultrasound in\\nlow-resource areas. Inaccurate estimations may lead to neg-\\native perinatal outcomes. We consider that machine learning\\ncan provide an accurate estimation for obstetricians alongside\\ntraditional clinical practices, as well as an efﬁcient and ef-\\nfective support tool for pregnant women for self-monitoring.\\nWe present a robust methodology using a data set compris-\\ning 4,212 intrapartum recordings. The cubic spline function\\nis used to ﬁt the curves of several key characteristics that are\\nextracted from ultrasound reports. A number of simple and\\npowerful machine learning algorithms are trained, and their\\nperformance is evaluated with real test data. We also propose\\na novel evaluation performance index called the intersection-\\nover-union (loU) for our study. The results are encouraging\\nusing an ensemble model consisting of Random Forest, XG-\\nBoost, and LightGBM algorithms. The experimental results\\nshow an loU of 0.64 between predicted range of fetal weight\\nat any gestational age from the ensemble model and that from\\nultrasound. Comparing with the ultrasound method, the esti-\\nmation accuracy is improved by 12%, and the mean relative\\nerror is reduced by 3%.\\n\\nIntroduction\\n\\nIn obstetrics, both abnormal fetal growth and fetal develop-\\nment are monitored via prenatal testing. However, there are\\nfew biomarkers that can be used to accurately predict the fe-\\ntal growth restrictions (FGR) (Conde-Agudelo et al. 2013),\\nmacrosomia, and other abnormalities. Currently, estimated\\nfetal weight (EFW) has became a central indicator for this\\npurpose. It is essential to obtain an accurate estimation of\\nantenatal fetal weight because potential complications may\\narise from excessive or low fetal birth weight during and af-\\nter delivery.\\n\\nThe prediction of a fetal birth weight just before the de-\\nlivery is able to effectively guide obstetricians to choose a\\nmore reasonable delivery mode for pregnant women. This\\nCopyright c(cid:13) 2019, Association for the Advancement of Artiﬁcial\\nIntelligence (www.aaai.org). All rights reserved.\\n\\ncan result in an improved delivery outcome during labour\\nand further reduce complications for mothers and infants af-\\nter labour (Pressman et al. 2000). Moreover, if the FGR and\\nadverse conditions such as intrauterine hypoxia can be de-\\ntected in time, it would be greatly beneﬁcial to further reduce\\nthe possibility of perinatal mortality of fetuses (Miller and\\nHuppi 2016). Therefore, it is desired that the EFW can be\\naccurate as possible not only at the end of the third trimester\\nbut also at any gestational week during pregnancy.\\n\\nSeveral methods can be used to predict fetal weight in\\nclinical practice, consisting of abdominal palpation, parturi-\\nent symphysio-fundal height and abdominal girth measure-\\nments, and obstetric ultrasound. Among them, the ultra-\\nsound based estimation method is most reliable and objec-\\ntive, and has been used extensively by obstetricians in China.\\nIts principle lies is the use of a class of well-established re-\\ngression models with multiple parameters standards for fe-\\ntuses. But, there a number of limitations of such method.\\nFirst, these regression models were proposed by different\\nclinicians, and are not generally applicable to all popula-\\ntions in the world. As a result, the direct use of such class\\nof models on Chinese population may result in inaccuracy,\\nparticularly for excessive or low fetal birth weight. Second,\\nthere are also strict requirements for sonographers and spe-\\nciﬁc standards for equipment for performing ultrasound ex-\\naminations. Factors like deformed fetal head, existence of\\noligohydramnios and abdominal fat, and poor image quality\\nmay all affect the ﬁnal estimation. Another limitation is that\\naccess to obstetric ultrasound remains poor in some most\\nlow-resource rural areas and this has signiﬁcantly affected\\nfetal weight estimation (Wanyonyi and Mutiso 2018).\\n\\nOther than the traditional methods introduced, machine\\nlearning techniques can be applied in this ﬁeld (Naimi,\\nPlatt, and Larkin 2018; Podda, Bacciu, and Micheli 2018;\\nZhu et al. 2018). The historical data of prenatal examinations\\ncan be analysed and the relationship between conceptual en-\\ntities can be explored through their own training, generalisa-\\ntion, self-organisation, and learning ability. Thus, they are a\\npreferable candidate to make more efﬁcient and reasonable\\ndecisions such as fetal weight estimation.\\n\\nThe main contributions of this paper are threefold. First,\\nwe establish a dataset consisting of 4,212 clinical records\\nbased on the electronic health record of pregnant women\\nfrom a large hospital in China. Second, we establish a tem-\\n\\n9522\\n\\n\\x0cporal relationship between the gestational age and the main\\ncharacteristics of fetal growth on Chinese population. The\\ncubic spline function method was used to ﬁt the relation-\\nship between characteristics such as the biparietal diameter\\n(BPD), abdominal circumference (AC), head circumference\\n(HC), and femur length (FL) and the gestational age. In ad-\\ndition, we also consider maternal physiological character-\\nistics, such as the pre-pregnancy body mass index (BMI),\\nuterine height and abdominal circumference. Third, we pro-\\npose an ensemble learning model, which has obtained better\\nprediction results than any single model. Our model is con-\\nstructed based on three machine learning algorithms and op-\\ntimised in parallel via a multi-parameter genetic algorithm,\\nand it has been evaluated on our real dataset and compared\\nto several other methods.\\n\\nMethodology\\n\\nPreprocessing\\nThe experimental data are obtained from Shenzhen Bao’an\\nMaternity & Child Healthcare Hospital. A total number of\\n5,000 samples from 2017 are randomly selected, and no gen-\\neral obstetrics, gynaecology and other general medical his-\\ntories regarding prenatal care are screened out. It was started\\nbefore 16 weeks of gestation, as measured by the menstrual\\ndate and nutritional health, including the maternal height\\n(≥153 cm), BMI (18.5 ≤ BM I < 30kg/m2), erythro-\\nprotein concentration (≥110 g/L), and whether the pregnant\\nwomen receive anaemia treatments, or have any special diet\\nrecipes. It can effectively reduce the risk factors in the FGR\\nand preterm birth.\\n\\nAt the same time, the distribution of pregnancy tests is not\\nequal, and their types are different during the long observa-\\ntion period of the pregnant women. To ensure sufﬁcient sam-\\nple distributions, the examination data must be after the 16th\\nweek of pregnancy. Effective preprocessing of the data is a\\nkey step to improve the accuracy of the prediction model.\\nParameters of Predictive Model A hospital identiﬁcation\\nnumber for the pregnant women is used as the main index to\\nextract the health records from the beginning of the preg-\\nnancy to the delivery for obtaining the birth weight. Y is\\ndeﬁned as the EFW from ultrasound examination, and X is\\ndeﬁned as the set of input parameters for the model. The ﬁ-\\nnal dataset X consists of 14 parameters, consisting of xh,\\nxpw, xp, xn, xa, xg, xgg, xf w, xpb, xcb, xBP D, xAC, xHC,\\nand xF L, and the meaning of each parameter is shown in\\nTable 1.\\nFeature Standardisation After data preprocessing, 4,212\\nsamples meet the underlying conditions. However, the dif-\\nferent physiological parameters have different units and or-\\nders of magnitude. To reduce these inﬂuences on the predic-\\ntion results, the data need to be normalised before the model\\nis trained to ensure that each feature is at the same order of\\nmagnitude. The normalisation is shown as Equation (1):\\n\\ny =\\n\\n2(x − xmin)\\nxmax − xmin\\n\\n− 1\\n\\nwhere x represents the current feature value, xmin and xmax\\nrepresent the minimum and maximum values of the current\\n\\nTable 1: Symbol deﬁnition of different parameters.\\nParameters\\nDeﬁnition\\nHeight of a pregnant woman (cm)\\nxh\\nWeight of a pregnant woman (kg)\\nxpw\\nGestational week\\nxp\\nNumber of pregnancy\\nxn\\nAge of a pregnant woman\\nxa\\nWeight gain of a pregnant woman (kg)\\nxg\\nFundal height of a pregnant woman\\nxgg\\nAbdominal circumference\\nxf w\\nBMI of pre-pregnancy\\nxpb\\nBMI of current pregnancy\\nxcb\\nFetal biparietal diameter (cm)\\nxBP D\\nFetal abdominal circumference (cm)\\nxAC\\nFetal head circumference (cm)\\nxHC\\nFetal femur length (cm)\\nxF L\\n\\nfeature, respectively, and y is the normalised feature value.\\nThe data range is [−1, 1].\\nConstruction of Fitted Function Despite the widespread\\nuse of ultrasound technology worldwide, people are con-\\ncerned about the low rate of detection of fetal developmental\\nabnormalities in routine clinical practice (Ewigman, Crane,\\nand Frigoletto 1993). However, there is a lack of appropri-\\nate international standards similar to those used to monitor\\ninfant growth (de Onis 2006). In addition, there are some dif-\\nferences in fetal growth characteristics in different regions.\\nTherefore, this study uses the cubic spline function method\\nto ﬁt four characteristics of ultrasound detection.\\n\\nSpeciﬁcally, at the interval [a, b], a = t0 < t1 < ... <\\ntn < t(n+1) = b, f (x) is deﬁned as a function of [tn, b]. If\\nf (x) meets the following two conditions: (1) f (x) is a cubic\\npolynomial on each interval of [a, t1], [t1, t2],..., [tn, b] and\\n(2) f (x) and its second derivative are continuous at ti(i =\\n1, 2, ..., n), then the piecewise polynomial function is called\\nthe cubic spline function. The point ti is called the node of\\nthe spline function. The cubic spline function can be shown\\nin Equation (2):\\n\\nf (x) = di(x − ti)3 + ci(x − ti)2 + bi(x − ti) + ai\\n\\n(2)\\nwhere ti ≤ x ≤ ti+1, i = 0, 1, ..., n. The sum of squared\\n\\nresiduals for ti is (cid:80)(yi − g(ti))2, and the penalised sum\\n\\nof the squares of the above selection functions is shown in\\nEquation (3):\\n\\nS(f ) =\\n\\n(yi − f (xi)) + γ\\n\\n(f(cid:48)(cid:48)(x))2dx\\n\\n(3)\\n\\n(cid:88)\\n\\n(cid:90) b\\n\\na\\n\\nFor a given smoothing parameter γ (whereby γ > 0),\\nthe estimation function f (x) minimises the values of S(f ),\\nwhich is referred to as a penalty least squares estimate. The\\nsmoothing parameter γ can be given by γ = CQ3/1000, C\\nis a given constant, and Q is the interquartile range of the\\nexplanatory variable.\\nEnsemble Machine Learning\\nEnsemble methods in machine learning that create multi-\\nple models are powerful prediction techniques since they\\n\\n(1)\\n\\n9523\\n\\n\\x0ccan increase the diversity of algorithms and reduce gener-\\nalisation error to improve the accuracy of the results (Diet-\\nterich 2000). This method is divided into stacking, blend-\\ning and voting. Ensemble methods have two basic elements:\\none is that the correlation between single models should be\\nas small as possible, and the other is that the performance\\nbetween single models is not too different. In practice, it is\\noften the case that a single model with a low correlation co-\\nefﬁcient and good performance can signiﬁcantly improve the\\nﬁnal prediction result.\\n\\nRandom forest\\n\\nis a supervised learning algorithm\\n(Breiman 2001). The random forest regression algorithm is\\na combined model, which incorporates a regression decision\\nsubtree. According to the principle of ensemble learning, the\\nmean of each decision subtree is taken as the regression pre-\\ndiction result. The random forest is a kind of bagging al-\\ngorithm, which focuses on reducing the variance. XGBoost\\n(Chen and Guestrin 2006) is a boosting algorithm (Schapire\\n1990), which focuses on reducing the bias. However, Light-\\nGBM (Ke, Meng, and Finley 2017) is a recently proposed\\nalgorithm. Therefore, the three classes of algorithms in this\\npaper satisfy the diversity, correlation, and performance re-\\nquirements. In this study, voting is used to construct an en-\\nsemble model, which is shown in Equation (4):\\n\\nhα(f ) = α0 + α1f i\\n\\n1 + α2f i\\n\\n2 + α3f i\\n3\\n\\n(4)\\n\\nwhere α1, α2, α3 are the weight parameters, α0 is a con-\\nstant, i represents the number of i-th samples i = 1, 2, ..., n\\nand f1, f2, f3 represent the predicted values of the random\\nforest, XGboost, and LightGBM models, respectively.\\n\\nOptimisation based on Genetic Algorithm\\nAccording to the above basic model analysis, the parameters\\nthat have a large impact on the prediction results of the ran-\\ndom forest model, consisting of the following: the maximum\\nnumber of features is used by a single decision tree δmax f ,\\nthe minimum number of leaf nodes δmin l, the maximum\\ndepth of the decision tree δmax d, and the minimum number\\nof samples required for the internal node subdivision δmin s.\\nFor the XGBoost model, the inﬂuence factor mainly in-\\ncludes the learning rate θeta, the maximum depth of the tree\\nθmax d, and the minimum leaf node sample weight θmin w.\\nRegarding the LlightGBM model, the inﬂuence factors\\nconsist of the tree model depth γmax d, the minimum num-\\nber of leaf nodes γmin l, the minimum leaf node weight\\nγmin w, and the learning rate γeta. If a traditional grid search\\nmethod is used to optimise 15 parameters, then optimisation\\ntakes a very long time. The genetic algorithm, as an intelli-\\ngent evolutionary algorithm, has a strong global search ca-\\npability. Therefore, this study proposes an ensemble model\\nbased on the multi-parameter parallel optimisation of the ge-\\nnetic algorithm. The speciﬁc steps are as follows:\\n1. Data preprocessing: the original data is preprocessed and\\n\\ndivided into a training set and a testing set.\\n\\n2. Initialise parameters of the genetic algorithm such as\\nthe population size, crossover probability, and mutation\\nprobability.\\n\\n3. Select the optimisation parameters and interval. Accord-\\ning to the above analysis, there are a total of 15 param-\\neters to be optimised: 4 parameters of the random forest\\nmodel, 3 parameters of the XGBoost model, 4 param-\\neters of the LightGBM model, and 4 parameters of the\\nensemble model. The optimal interval is determined by\\nchromosome coding.\\n\\n4. Determine the ﬁtness function. Calculate the average rel-\\native error between the predicted value and the true value,\\nso the ﬁtness function is shown in Equation (5):\\n1 − yi\\nyi\\n\\nhα(f i) − yi\\n\\nM AP E =\\n\\n|\\n\\n(5)\\n\\nn(cid:88)\\n(\\n2 − yi\\nyi\\n\\n1\\nn\\n+ | f i\\n\\ni=1\\n\\nyi\\n| + | f i\\n\\n+ | f i\\n3 − yi\\nyi\\n\\n|)\\n\\nwhere hα(f i) represents the ensemble model predictive\\nvalue, and yi denotes the true value. Moreover, f i\\n2, f i\\n3\\nare the output values of the random forest, XGBoost, and\\nLightGBM models, respectively, and n is the number of\\ntraining sets.\\n\\n1, f i\\n\\n5. Parameter optimisation: First, decode the chromosomes\\nin the population; then calculate the ﬁtness value of each\\ngeneration of the population, and perform the survival of\\nthe ﬁttest. Finally, determine whether the population per-\\nformance satisﬁes the maximum number of genetics, and\\nif so, the optimal parameter is output; otherwise, accord-\\ning to the genetic strategy, the selection, crossover and\\nmutation operations are used to obtain the offspring.\\n\\n6. Result judgement: if the MAPE error requirement is sat-\\nisﬁed, then the optimisation is ﬁnished. Otherwise, repeat\\nstep 4.\\n\\n7. Input the test sample to obtain the best prediction result.\\n\\nThe detailed process is shown in Figure 1.\\n\\nPerformance Evaluation Index\\nThis paper uses two indices to measure the performance of\\nthe ensemble model. The ﬁrst index is the mean relative er-\\nror (MRE), which is a measure of the credibility. If n is the\\nnumber of samples, then the MRE is shown in Equation (6):\\n\\nn(cid:88)\\n\\ni=1\\n\\n1\\nn\\n\\n|ytrue − ypred|\\n\\nytrue\\n\\nM RE =\\n\\nwhere ytrue denotes the true label and ypred denotes the pre-\\ndicted fetal weight.\\n\\nTo better reﬂect the coincidence between different inter-\\nvals, this paper introduces a novel concept, originally used\\nin the ﬁeld of image processing, namely, loU. This method\\ncan reﬂect the coincidence degree of different learning al-\\ngorithms for predicting the fetal weight interval, and it is\\nshown in Equation (7):\\n\\n(6)\\n\\n(7)\\n\\nIoU =\\n\\nscope ∩ f 2\\nf 1\\nscope ∪ f 2\\nf 1\\n\\nscope\\n\\nscope\\n\\nwhere f 1\\nthe algorithm model and f 2\\nrange of the ultrasound examination.\\n\\nscope represents the fetal weight prediction range of\\nscope represents the fetal weight\\n\\n9524\\n\\n\\x0cFigure 2: Four feature ﬁtting curves.\\n\\n0.20. The minimum R2 of the AC is 0.955, and the maxi-\\nmum MRE is 0.22. The minimum R2 of the HC is 0.950,\\nand the maximum MRE is 0.24. The minimum R2 of the FL\\nis 0.951, and the maximum MRE is 0.16. The R2 of each\\nindex is above 0.95, and the MRE is within the tolerance;\\nthus, the ﬁtting result is satisfactory.\\nEvaluation of Prediction\\nThe random forest, XGboost, LightGBM models and the en-\\nsemble model are based on the genetic algorithm. Multi-\\nparameter parallel optimisation is used to predict the fetal\\nweight, which is compared with the multi-parameter for-\\nmula (Hadlock 1990) used in an ultrasonic examination. The\\nexperimental results are shown in Figure 3.\\n\\nAs shown in Figure 3, the MRE based on the single ma-\\nchine learning algorithm model is approximately 8%. The\\nMRE of the formula method in (Hadlock 1990) is 14.6%.\\nThe MRE of the ensemble model is approximately 6%. In\\nthe absence of ultrasound detection, the ﬁtting function is\\nused to ﬁt the four eigenvalues as shown in Figure 2, and\\nthen the integrated model is used to predict the fetal weight\\nrange. The loU index is used to prove the effectiveness of\\nthe algorithm (see Table 3).\\n\\nIn Table 3, in the absence of an ultrasound examina-\\ntion, the ensemble model, can predict the fetal weight range.\\nCompared with the ultrasonic examination, the loU value is\\ngreater than 0.6. To some extent, the fetal weight can be pre-\\ndicted at any gestation according to the maternal character-\\nistic parameters and the ﬁtted four ultrasonic characteristic\\nvalues. The prediction results of some samples are shown in\\nFigure 4.\\n\\nIn Figure 4, the “0”, “1”, “2”, “3” and “4” values on the\\nhorizontal axis represent the ultrasonic examination, the en-\\nsemble model in this paper, the XGBoost, the LightGBM,\\n\\nFigure 1: Fetal weight estimation process based on the ge-\\nnetic algorithm.\\n\\nResults\\n\\nBased on the screening steps in the previous sections, a to-\\ntal of 4,212 samples were selected, of which 3,370 samples\\nare used as the training sets and 842 samples are used as the\\ntest sets. Then, a cubic spline function was used to establish\\na functional relationship between the four indexes of ultra-\\nsound examination and the pregnancy. The ﬁtting results are\\nshown in Figure 2.\\n\\nThe ﬁtting results of the percentile curves are shown in\\nTable 2. Among the percentiles, the R2 (determination coef-\\nﬁcient) of the BPD is at least 0.953, and the MRE is at most\\n\\n9525\\n\\n\\x0cTable 2: Fitting results of each percentile curve.\\n\\nCentile\\n\\nP95\\nP75\\nP50\\nP25\\nP10\\n\\nBPD\\nγ MER\\n0.16\\n0.12\\n0.09\\n0.20\\n0.15\\n\\n0.3\\n0.2\\n0.1\\n0.2\\n0.3\\n\\nR2\\n0.954\\n0.960\\n0.965\\n0.953\\n0.955\\n\\nAC\\nγ MER\\n0.22\\n0.18\\n0.13\\n0.17\\n0.23\\n\\n0.4\\n0.3\\n0.2\\n0.3\\n0.1\\n\\nR2\\n0.958\\n0.966\\n0.970\\n0.963\\n0.955\\n\\nHC\\nγ MER\\n0.20\\n0.17\\n0.15\\n0.24\\n0.18\\n\\n0.4\\n0.2\\n0.2\\n0.3\\n0.1\\n\\nR2\\n0.956\\n0.961\\n0.962\\n0.950\\n0.957\\n\\nFL\\nγ MER\\n0.16\\n0.12\\n0.08\\n0.11\\n0.09\\n\\n0.4\\n0.3\\n0.2\\n0.1\\n0.2\\n\\nR2\\n0.951\\n0.953\\n0.967\\n0.955\\n0.960\\n\\nTable 3: IoU based on different machine learning algorithms.\\n\\nFigure 4: Mean relative error of different models.\\n\\nFigure 3: Mean relative error of different models.\\n\\nAlgorithms\\nRandom forest\\nXGBoost\\nLightGBM\\nOur model\\n\\nIntersection-over-Union\\n0.607\\n0.623\\n0.610\\n0.650\\n\\nand the random forest models, respectively, and the ordi-\\nnate expresses by the predicted fetal weight range. From\\nthe graph results, after optimising the multi-model param-\\neters based on the genetic algorithm, the advantages of each\\nmodel can be effectively utilised, so that the fetal weight\\nprediction interval is closer to the fetal weight range of the\\nultrasound examination.\\nAnalysis of Fetal Growth Change\\nThe fetal growth curve is an important index of the fetal\\nhealth status, which can provide a basis for early diagno-\\nsis and the prevention of fetal abnormalities. At the same\\ntime, pregnant women can observe the trend of fetal weight\\nchanges in each gestational week, including the average fe-\\ntal weight curve, the 10th percentile curve and the 90th per-\\ncentile curve. Therefore, based on the characteristic param-\\neters of pregnant women and the ﬁtted ultrasound charac-\\n\\nteristic parameters, this study uses the ensemble model to\\npredict the fetal weight at the current moment and to timely\\nunderstand the trend of fetal growth.\\n\\nWhen comparing with the 10th and 90th percentiles of\\nChina’s fetal growth standard curve (Lei and Wen 1998),\\nif the curve is lower than the 10th percentile, the fetus is\\nsmall for its gestational age (SGA), and conversely, when the\\ncurve is greater than the 90th percentile, the fetus is large for\\nits gestational age (LGA). A sample was randomly selected\\nfrom the testing set, and the fetal weight is predicted by the\\nensemble model proposed in this study. The experimental\\nresults are shown in Figure 5.\\n\\nDiscussion\\n\\nTo verify the superiority of the model, the ensemble model is\\nused to predict the birth weight of the fetus. From the testing\\nsets, 527 samples have a record of an ultrasound examina-\\ntion within one week prior to delivery. In addition, it is more\\nobjective to evaluate the accuracy of different algorithms,\\nthis study uses another criterion, that is, the error between\\nthe predicted and actual fetal weight is within ±250g, and\\nthe prediction is considered to be accurate (Jain, Duin, and\\n\\n9526\\n\\n\\x0cAcknowledgements\\n\\nThis work is supported by the National Natural Sci-\\nence Foundation of China (No. 61272328) and 2017 Sci-\\nence and Technology Plan Project of Shenzhen, China\\n(KJYY20170721163528274).\\n\\nReferences\\n\\nBreiman, L. 2001. Random Forests. Machine Learning 45(1):5–32.\\nChen, T., and Guestrin, C. 2006. XGBoost: A Scalable Tree Boost-\\ning System. In KDD 2016, 785–794.\\nConde-Agudelo, A.; Papageorghiou, A. T.; Kennedy, S. H.; and\\nVillar, J.\\n2013. Novel biomarkers for predicting intrauterine\\ngrowth restriction: a systematic review and meta-analysis. BJOG\\n120(6):681–694.\\nde Onis, M.\\n2006. WHO Child Growth Standards based\\non length/height, weight and age. Acta Paediatrica 95(S450):\\n76–85.\\nDietterich, T. G. 2000. An Experimental Comparison of Three\\nMethods for Constructing Ensembles of Decision Trees: Bagging,\\nBoosting, and Randomization. Machine Learning 40(2):139–157.\\nEwigman, B. G.; Crane, J. P.; and Frigoletto, F. D. 1993. Effect\\nof Prenatal Ultrasound Screening on Perinatal Outcome. The New\\nEngland Journal of Medicine 329(12):821–827.\\nHadlock, F. P. 1990. Sonographic estimation of fetal age and\\nweight. Radiologic Clinics of North America 28(1):39–50.\\nJain, A. K.; Duin, R. P. W.; and Mao, J. 2000. Statistical pattern\\nrecognition: a review. IEEE T. PAMI 22(1):4–37.\\nKe, G.; Meng, Q.; and Finley, T. 2017. LightGBM: A Highly\\nEfﬁcient Gradient Boosting Decision Tree. In NIPS 2017.\\nLei, H., and Wen, S. W. 1998. Ultrasonographic examination of\\nintrauterine growth for multiple fetal dimensions in a chinese pop-\\nulation. AJOG 178(5):916–921.\\nMiller, S. L., and Huppi, P. S. 2016. The consequences of fetal\\ngrowth restriction on brain structure and neurodevelopmental out-\\ncome. Journal of Physiology 594(4):807–823.\\nNaimi, A. I.; Platt, R. W.; and Larkin, J. C.\\n2018. Ma-\\nchine Learning for Fetal Growth Prediction. Epidemiology 29(2):\\n290–298.\\nPodda, M.; Bacciu, D.; and Micheli, A.\\nA ma-\\nchine learning approach to estimating preterm infants survival:\\ndevelopment of the Preterm Infants Survival Assessment (PISA)\\npredictor. Scientiﬁc Reports 8(13743):1–9.\\nPressman, E. K.; Bienstock, J. L.; Blakemore, K. J.; Martin, S. A.;\\nand Callan, N. A. 2000. Prediction of birth weight by ultrasound\\nin the third trimester. Obstetrics & Gynecology 95(4):502–506.\\nSchapire, R. E. 1990. The strength of weak learnability. Machine\\nLearning 5(2):197–227.\\nWanyonyi, S. Z., and Mutiso, S. K. 2018. Monitoring fetal growth\\nin settings with limited ultrasound access. Best P&R Clinical Ob-\\nstetrics & Gynaecology 49:29–36.\\nZhu, H.; Tao, J.; Yu, K.; and et al. 2018. Fetal Weight Prediction\\nAnalysis Based on GA-BP Neural Networks. Computer Systems &\\nApplications 27(3):162–167.\\n\\n2018.\\n\\nFigure 5: Fetal growth prediction curve.\\n\\nMao 2000). Therefore, different algorithms are used to pre-\\ndict fetal birth weight. The experimental results are shown\\nin Table 4.\\n\\nTable 4: Different methods that predict the fetal birth weight.\\n\\nParameters\\nHadlock (Hadlock 1990)\\nGA-BP (Zhu et al. 2018)\\nRandom forest\\nXGBoost\\nLightGBM\\nProposed ensemble model\\n\\nMRE (%) Accuracy (%)\\n52.3\\n63.1\\n60.0\\n62.1\\n59.4\\n64.3\\n\\n10.2\\n7.5\\n8.3\\n8.2\\n8.4\\n7.0\\n\\nTable 4 shows that the ensemble model proposed in this\\npaper predicts the fetal birth weight and has a certain degree\\nof improvement in the MRE and accuracy compared with\\nthe single machine learning algorithm model and the multi-\\nparameter method. The MRE is reduced by approximately\\n3%, and the accuracy is improved by approximately 12%.\\n\\nConclusion\\n\\nThis paper proposed a novel approach to estimating fetal\\nweight using ensemble machine learning algorithms. The\\ncubic spline function has been used to ﬁt the functional re-\\nlationship between the BPD, AC, HC, and FL and the ges-\\ntational age based on the health records of pregnant women.\\nAn ensemble machine learning model has been proposed\\nbased on the genetic algorithm with parallel optimisation\\nof multiple parameters to predict the fetal weight at vary-\\ning gestational age. We have also evaluated the applicability\\nof the ensemble model for the domain of on real datasets.\\nComparing with the traditional ultrasound-based estimation\\nmethods, it obtains the EFW more accurately and efﬁciently.\\nNext, there are further clinical tests in different hospitals,\\nand software for both home and hospital applications are\\nunder development and soon to be deployed. Estimation of\\nfetal birth weight among twins is another direction of future\\nresearch.\\n\\n9527\\n\\n\\x0c'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pdfminer.high_level import extract_text\n",
    "extract_text('test1.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.system('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年 01月 19日 星期日 13:41:41 CST\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "nowtime = os.popen('date')\n",
    "print (nowtime.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qpdf version 8.0.2\n",
      "Run qpdf --copyright to see copyright and license information.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "nowtime = os.popen('qpdf -version')\n",
    "print (nowtime.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_file = 'test.pdf'\n",
    "to_file = 'test1.pdf'\n",
    "cmd = f'qpdf --linearize {from_file} {to_file}'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "nowtime = os.popen(cmd)\n",
    "print (nowtime.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual\\n\\nReading Comprehension on Novels\\n\\nSchool of Computer Science and Technology, Soochow University, Suzhou, China\\n\\nYimin Jing , Deyi Xiong∗ and Yan Zhen\\n{yymmjing,corezhen}@gmail.com\\n\\ndyxiong@suda.edu.cn\\n\\nAbstract\\n\\nThis paper presents BiPaR, a bilingual parallel\\nnovel-style machine reading comprehension\\n(MRC) dataset, developed to support multi-\\nlingual and cross-lingual reading comprehen-\\nsion. The biggest difference between BiPaR\\nand existing reading comprehension datasets is\\nthat each triple (Passage, Question, Answer) in\\nBiPaR is written parallelly in two languages.\\nWe collect 3,667 bilingual parallel paragraphs\\nfrom Chinese and English novels, from which\\nwe construct 14,668 parallel question-answer\\npairs via crowdsourced workers following a\\nstrict quality control procedure. We analyze\\nBiPaR in depth and ﬁnd that BiPaR offers\\ngood diversiﬁcation in preﬁxes of questions,\\nanswer types and relationships between ques-\\ntions and passages. We also observe that\\nanswering questions of novels requires read-\\ning comprehension skills of coreference res-\\nolution, multi-sentence reasoning, and under-\\nstanding of implicit causality, etc. With Bi-\\nPaR, we build monolingual, multilingual, and\\ncross-lingual MRC baseline models. Even for\\nthe relatively simple monolingual MRC on this\\ndataset, experiments show that a strong BERT\\nbaseline is over 30 points behind human in\\nterms of both EM and F1 score, indicating\\nthat BiPaR provides a challenging testbed for\\nmonolingual, multilingual and cross-lingual\\nMRC on novels. The dataset is available at\\nhttps://multinlp.github.io/BiPaR/.\\n\\n1\\n\\nIntroduction\\n\\nMachine reading comprehension is to evaluate\\nhow well computer systems understand natural\\nlanguage texts, where machines read a given text\\npassage and answer questions about the passage.\\nIt has been regarded as a crucial technology for\\nmany applications such as question answering, di-\\nalogue systems (Nguyen et al., 2016; Chen et al.,\\n\\n∗Corresponding author\\n\\nFigure 1: Illustration of BiPaR with the monolingual,\\nmultilingual and cross-lingual MRC on the dataset.\\n2017; Liu et al., 2018; Wang et al., 2018) and\\nso on. In order to enable machine to understand\\ntexts, large-scale reading comprehension datasets\\nhave been developed, such as CNN/Daily Mail\\n(Hermann et al., 2015), SQuAD (Rajpurkar et al.,\\n2016), MS MACRO (Nguyen et al., 2016), hot-\\npotQA (Yang et al., 2018), CoQA (Reddy et al.,\\n2019), etc.\\n\\nThe majority of such datasets, unfortunately,\\nare only for monolingual text understanding. To\\nthe best of our knowledge, there is no publicly\\navailable bilingual parallel reading comprehension\\ndataset, which is exactly what BiPaR is mainly\\ndeveloped for, as illustrated in Figure 1. BiPaR\\n\\nProceedingsofthe2019ConferenceonEmpiricalMethodsinNaturalLanguageProcessingandthe9thInternationalJointConferenceonNaturalLanguageProcessing,pages2452–2462,HongKong,China,November3–7,2019.c(cid:13)2019AssociationforComputationalLinguistics2452(cid:9056)(cid:3937)(cid:1264)(cid:17)(cid:17)(cid:17)(cid:263)(cid:11443)(cid:21967)(cid:1461)(cid:952)(cid:17935)(cid:7269)(cid:7055)(cid:1137)(cid:11450)(cid:1226)(cid:21967)(cid:1306)(cid:17)(cid:17)(cid:17)(cid:12545)(cid:2261)(cid:1153)(cid:2628)(cid:952)(cid:4668)(cid:1306)(cid:13674)(cid:3348)(cid:574)(cid:264)(cid:19996)(cid:4677)(cid:4563)(cid:5322)(cid:18057)(cid:966)(cid:263)(cid:7269)(cid:574)(cid:264)(cid:17)(cid:17)(cid:17)(cid:1087)(cid:1264)(cid:7352)(cid:11151)(cid:952)(cid:1423)(cid:1264)(cid:17974)(cid:2545)(cid:574)(cid:264)(cid:7190)(cid:7791)(cid:18057)(cid:1264)(cid:2754)(cid:21767)(cid:21967)(cid:1461)(cid:573)(cid:21754)(cid:21967)(cid:1461)(cid:1087)(cid:1264)(cid:16002)(cid:12146)(cid:17974)(cid:2096)(cid:574)(cid:48)(cid:68)(cid:71)(cid:68)(cid:80)(cid:72)(cid:3)(cid:17)(cid:17)(cid:17)(cid:55)(cid:75)(cid:76)(cid:86)(cid:3)(cid:76)(cid:86)(cid:3)(cid:87)(cid:75)(cid:72)(cid:3)(cid:47)(cid:72)(cid:68)(cid:71)(cid:72)(cid:85)(cid:10)(cid:86)(cid:3)(cid:41)(cid:76)(cid:89)(cid:72)(cid:3)(cid:39)(cid:85)(cid:68)(cid:74)(cid:82)(cid:81)(cid:3)(cid:39)(cid:76)(cid:86)(cid:70)(cid:15)(cid:3)(cid:58)(cid:75)(cid:76)(cid:87)(cid:72)(cid:3)(cid:39)(cid:85)(cid:68)(cid:74)(cid:82)(cid:81)(cid:3)(cid:48)(cid:68)(cid:85)(cid:86)(cid:75)(cid:68)(cid:79)(cid:15)(cid:10)(cid:3)(cid:86)(cid:75)(cid:72)(cid:3)(cid:86)(cid:68)(cid:76)(cid:71)(cid:17)(cid:3)(cid:17)(cid:17)(cid:17)(cid:60)(cid:82)(cid:88)(cid:3)(cid:68)(cid:85)(cid:72)(cid:3)(cid:87)(cid:82)(cid:3)(cid:85)(cid:72)(cid:87)(cid:88)(cid:85)(cid:81)(cid:3)(cid:76)(cid:87)(cid:3)(cid:90)(cid:75)(cid:72)(cid:81)(cid:3)(cid:92)(cid:82)(cid:88)(cid:3)(cid:75)(cid:68)(cid:89)(cid:72)(cid:3)(cid:70)(cid:82)(cid:80)(cid:83)(cid:79)(cid:72)(cid:87)(cid:72)(cid:71)(cid:3)(cid:92)(cid:82)(cid:88)(cid:85)(cid:3)(cid:80)(cid:76)(cid:86)(cid:86)(cid:76)(cid:82)(cid:81)(cid:17)(cid:10)(cid:3)(cid:10)(cid:60)(cid:72)(cid:86)(cid:15)(cid:10)(cid:3)(cid:85)(cid:72)(cid:83)(cid:79)(cid:76)(cid:72)(cid:71)(cid:3)(cid:55)(cid:85)(cid:76)(cid:81)(cid:78)(cid:72)(cid:87)(cid:15)(cid:3)(cid:3)(cid:17)(cid:17)(cid:17)(cid:3)(cid:44)(cid:3)(cid:90)(cid:68)(cid:81)(cid:87)(cid:3)(cid:92)(cid:82)(cid:88)(cid:3)(cid:87)(cid:82)(cid:3)(cid:86)(cid:87)(cid:68)(cid:92)(cid:3)(cid:75)(cid:72)(cid:85)(cid:72)(cid:3)(cid:73)(cid:82)(cid:85)(cid:3)(cid:68)(cid:3)(cid:90)(cid:75)(cid:76)(cid:79)(cid:72)(cid:15)(cid:10)(cid:3)(cid:48)(cid:68)(cid:71)(cid:68)(cid:80)(cid:72)(cid:3)(cid:43)(cid:82)(cid:81)(cid:74)(cid:3)(cid:82)(cid:85)(cid:71)(cid:72)(cid:85)(cid:72)(cid:71)(cid:17)(cid:3)(cid:55)(cid:75)(cid:72)(cid:3)(cid:85)(cid:72)(cid:86)(cid:87)(cid:3)(cid:82)(cid:73)(cid:3)(cid:92)(cid:82)(cid:88)(cid:3)(cid:80)(cid:68)(cid:92)(cid:3)(cid:79)(cid:72)(cid:68)(cid:89)(cid:72)(cid:17)(cid:529)(cid:53)(cid:82)(cid:82)(cid:87)(cid:79)(cid:72)(cid:86)(cid:86)(cid:15)(cid:3)(cid:37)(cid:79)(cid:68)(cid:70)(cid:78)(cid:3)(cid:39)(cid:85)(cid:68)(cid:74)(cid:82)(cid:81)(cid:15)(cid:3)(cid:68)(cid:81)(cid:71)(cid:3)(cid:60)(cid:72)(cid:79)(cid:79)(cid:82)(cid:90)(cid:3)(cid:39)(cid:85)(cid:68)(cid:74)(cid:82)(cid:81)(cid:3)(cid:86)(cid:68)(cid:79)(cid:88)(cid:87)(cid:72)(cid:71)(cid:3)(cid:68)(cid:81)(cid:71)(cid:3)(cid:79)(cid:72)(cid:73)(cid:87)(cid:17)(cid:52)(cid:72)(cid:81)(cid:29)(cid:3)(cid:58)(cid:75)(cid:68)(cid:87)(cid:3)(cid:71)(cid:76)(cid:71)(cid:3)(cid:48)(cid:68)(cid:71)(cid:68)(cid:80)(cid:72)(cid:3)(cid:43)(cid:82)(cid:81)(cid:74)(cid:3)(cid:74)(cid:76)(cid:89)(cid:72)(cid:3)(cid:87)(cid:82)(cid:3)(cid:55)(cid:85)(cid:76)(cid:81)(cid:78)(cid:72)(cid:87)(cid:34)(cid:3)(cid:36)(cid:72)(cid:81)(cid:29)(cid:3)(cid:41)(cid:76)(cid:89)(cid:72)(cid:3)(cid:39)(cid:85)(cid:68)(cid:74)(cid:82)(cid:81)(cid:3)(cid:39)(cid:76)(cid:86)(cid:70)(cid:52)(cid:72)(cid:81)(cid:16)(cid:52)(cid:93)(cid:75)(cid:29)(cid:3)(cid:58)(cid:75)(cid:68)(cid:87)(cid:3)(cid:71)(cid:76)(cid:71)(cid:3)(cid:48)(cid:68)(cid:71)(cid:68)(cid:80)(cid:72)(cid:3)(cid:43)(cid:82)(cid:81)(cid:74)(cid:3)(cid:74)(cid:76)(cid:89)(cid:72)(cid:3)(cid:87)(cid:82)(cid:3)(cid:55)(cid:85)(cid:76)(cid:81)(cid:78)(cid:72)(cid:87)(cid:34)(cid:3)(cid:18)(cid:3)(cid:3)(cid:9056)(cid:3937)(cid:1264)(cid:4668)(cid:1270)(cid:1150)(cid:1106)(cid:16309)(cid:13583)(cid:1212)(cid:19996)(cid:4677)(cid:4563)(cid:971)(cid:36)(cid:72)(cid:81)(cid:16)(cid:36)(cid:93)(cid:75)(cid:29)(cid:3)(cid:41)(cid:76)(cid:89)(cid:72)(cid:3)(cid:39)(cid:85)(cid:68)(cid:74)(cid:82)(cid:81)(cid:3)(cid:39)(cid:76)(cid:86)(cid:70)(cid:3)(cid:18)(cid:3)(cid:1226)(cid:21967)(cid:1306)(cid:52)(cid:72)(cid:81)(cid:29)(cid:3)(cid:58)(cid:75)(cid:82)(cid:3)(cid:71)(cid:76)(cid:71)(cid:3)(cid:48)(cid:68)(cid:71)(cid:68)(cid:80)(cid:72)(cid:3)(cid:43)(cid:82)(cid:81)(cid:74)(cid:3)(cid:68)(cid:79)(cid:79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      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pdfminer.high_level import extract_text\n",
    "a = extract_text('/home/d/papers_data/EMNLP/EMNLP_2019/BiPaR  A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels.pdf', maxpages=1, caching=True)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    print(len(res_json_text))\n",
    "    _part = 1\n",
    "    for i,r in enumerate(res_json_text):\n",
    "        _temp = []\n",
    "        _step = 300\n",
    "        _years = '_'.join(years)\n",
    "        if ((i+1) % _step == 0) or ((i+1) == len(res_json_text)):\n",
    "            with open(f'./{folder_name}/{org}_{_years}_{_part}.json', 'w', encoding='utf8') as f:\n",
    "                f.write(''.join(_temp))\n",
    "            _temp = []\n",
    "            _part += 1\n",
    "        else: \n",
    "            _temp.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d42b367015fb45e5940a11f9bdef2143",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='writ to file', max=3000, style=ProgressStyle(description_widt…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "res_json_text = [str(i) for i in range(3000)]\n",
    "years = [str(i) for i in range(2014,2020)]\n",
    "\n",
    "_part = 1\n",
    "for i,r in enumerate(tqdm(res_json_text, desc = 'writ to file')):\n",
    "    _temp = []\n",
    "    _step = 300\n",
    "    _years = '_'.join(years)\n",
    "    if ((i+1) % _step == 0) or ((i+1) == len(res_json_text)):\n",
    "        with open(f'./test/test_{_part}.json', 'w', encoding='utf8') as f:\n",
    "            f.write(''.join(_temp))\n",
    "        _temp = []\n",
    "        _part += 1\n",
    "    else: \n",
    "        _temp.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "299"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "299 % 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mskipkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mensure_ascii\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcheck_circular\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mallow_nan\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mindent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mseparators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msort_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Serialize ``obj`` as a JSON formatted stream to ``fp`` (a\n",
       "``.write()``-supporting file-like object).\n",
       "\n",
       "If ``skipkeys`` is true then ``dict`` keys that are not basic types\n",
       "(``str``, ``int``, ``float``, ``bool``, ``None``) will be skipped\n",
       "instead of raising a ``TypeError``.\n",
       "\n",
       "If ``ensure_ascii`` is false, then the strings written to ``fp`` can\n",
       "contain non-ASCII characters if they appear in strings contained in\n",
       "``obj``. Otherwise, all such characters are escaped in JSON strings.\n",
       "\n",
       "If ``check_circular`` is false, then the circular reference check\n",
       "for container types will be skipped and a circular reference will\n",
       "result in an ``OverflowError`` (or worse).\n",
       "\n",
       "If ``allow_nan`` is false, then it will be a ``ValueError`` to\n",
       "serialize out of range ``float`` values (``nan``, ``inf``, ``-inf``)\n",
       "in strict compliance of the JSON specification, instead of using the\n",
       "JavaScript equivalents (``NaN``, ``Infinity``, ``-Infinity``).\n",
       "\n",
       "If ``indent`` is a non-negative integer, then JSON array elements and\n",
       "object members will be pretty-printed with that indent level. An indent\n",
       "level of 0 will only insert newlines. ``None`` is the most compact\n",
       "representation.\n",
       "\n",
       "If specified, ``separators`` should be an ``(item_separator, key_separator)``\n",
       "tuple.  The default is ``(', ', ': ')`` if *indent* is ``None`` and\n",
       "``(',', ': ')`` otherwise.  To get the most compact JSON representation,\n",
       "you should specify ``(',', ':')`` to eliminate whitespace.\n",
       "\n",
       "``default(obj)`` is a function that should return a serializable version\n",
       "of obj or raise TypeError. The default simply raises TypeError.\n",
       "\n",
       "If *sort_keys* is true (default: ``False``), then the output of\n",
       "dictionaries will be sorted by key.\n",
       "\n",
       "To use a custom ``JSONEncoder`` subclass (e.g. one that overrides the\n",
       "``.default()`` method to serialize additional types), specify it with\n",
       "the ``cls`` kwarg; otherwise ``JSONEncoder`` is used.\n",
       "\u001b[0;31mFile:\u001b[0m      /home/anaconda3/lib/python3.7/json/__init__.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "json.dump?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./test/文件夹 (3)\\\\BBB (1).txt'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "tt = './test/文件夹 (3)\\@@#1#@@BBB (1).txt'\n",
    "\n",
    "re.sub(r'@@#(.*?)#@@', '',tt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = ['sss'.count('s') ,2,3]\n",
    "sum(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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